Last updated on 2022-03-18 06:49:01 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 5.1.7 | 52.14 | 503.07 | 555.21 | ERROR | |
| r-devel-linux-x86_64-debian-gcc | 5.1.7 | 42.40 | 367.38 | 409.78 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 5.1.7 | 678.47 | ERROR | |||
| r-devel-linux-x86_64-fedora-gcc | 5.1.7 | 768.98 | ERROR | |||
| r-devel-windows-x86_64-new-UL | 5.1.7 | 151.00 | 801.00 | 952.00 | ERROR | |
| r-devel-windows-x86_64-new-TK | 5.1.7 | ERROR | ||||
| r-patched-linux-x86_64 | 5.1.7 | 51.01 | 463.53 | 514.54 | OK | |
| r-release-linux-x86_64 | 5.1.7 | 45.05 | 469.39 | 514.44 | OK | |
| r-release-macos-arm64 | 5.1.7 | OK | ||||
| r-release-macos-x86_64 | 5.1.7 | OK | ||||
| r-release-windows-ix86+x86_64 | 5.1.7 | 90.00 | 545.00 | 635.00 | OK | |
| r-oldrel-macos-x86_64 | 5.1.7 | OK | ||||
| r-oldrel-windows-ix86+x86_64 | 5.1.7 | 95.00 | 559.00 | 654.00 | OK |
Version: 5.1.7
Check: package dependencies
Result: NOTE
Package suggested but not available for checking: 'optmatch'
Flavor: r-devel-linux-x86_64-debian-clang
Version: 5.1.7
Check: examples
Result: ERROR
Running examples in 'Zelig-Ex.R' failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: Zelig-mlogit-bayes-class
> ### Title: Bayesian Multinomial Logistic Regression
> ### Aliases: Zelig-mlogit-bayes-class zmlogitbayes
>
> ### ** Examples
>
> data(mexico)
> z.out <- zelig(vote88 ~ pristr + othcok + othsocok,model = "mlogit.bayes",
+ data = mexico,verbose = FALSE)
Calculating MLEs and large sample var-cov matrix.
This may take a moment...
Inverting Hessian to get large sample var-cov matrix.
Calculating MLEs and large sample var-cov matrix.
This may take a moment...
Inverting Hessian to get large sample var-cov matrix.
Error in if (mcmc.method == "RWM") { : the condition has length > 1
Calls: zelig ... do.grouped_df -> eval_tidy -> eval -> eval -> <Anonymous>
Execution halted
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc
Version: 5.1.7
Check: tests
Result: ERROR
Running 'testthat.R' [210s/235s]
Running the tests in 'tests/testthat.R' failed.
Complete output:
> library(AER)
Loading required package: car
Loading required package: carData
Loading required package: lmtest
Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, as.Date.numeric
Loading required package: sandwich
Loading required package: survival
> library(dplyr)
Attaching package: 'dplyr'
The following object is masked from 'package:car':
recode
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
> library(geepack)
> library(survey)
Loading required package: grid
Loading required package: Matrix
Attaching package: 'survey'
The following object is masked from 'package:graphics':
dotchart
> library(testthat)
Attaching package: 'testthat'
The following object is masked from 'package:dplyr':
matches
>
> set.seed(123)
> test_check("Zelig")
Loading required package: Zelig
-- Imputation 1 --
1 2 3
-- Imputation 2 --
1 2 3
-- Imputation 3 --
1 2 3
-- Imputation 4 --
1 2 3
-- Imputation 5 --
1 2 3
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
-- Imputation 1 --
1 2
-- Imputation 2 --
1 2 3
-- Imputation 3 --
1 2
-- Imputation 4 --
1 2 3
-- Imputation 5 --
1 2
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Ben Goodrich, and Ying Lu. 2013.
normal-bayes: Bayesian Normal Linear Regression
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Andrew D. Martin, Kevin M. Quinn, and Jong Hee Park. 2013.
factor-bayes: Bayesian Factor Analysis
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Terry M. Therneau, and Thomas Lumley. 2011.
exp: Exponential Regression for Duration Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
gamma: Gamma Regression for Continuous, Positive Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
Calculating MLEs and large sample var-cov matrix.
This may take a moment...
Inverting Hessian to get large sample var-cov matrix.
Calculating MLEs and large sample var-cov matrix.
This may take a moment...
Inverting Hessian to get large sample var-cov matrix.
How to cite this model in Zelig:
Christian Kleiber and Achim Zeileis. 2008.
ivreg: Instrumental-Variable Regression
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Christian Kleiber and Achim Zeileis. 2008.
ivreg: Instrumental-Variable Regression
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
Model:
Call:
stats::glm(formula = vote ~ age + race, family = binomial("logit"),
data = as.data.frame(.))
Deviance Residuals:
Min 1Q Median 3Q Max
-1.9268 -1.2962 0.7072 0.7766 1.0723
Coefficients:
Estimate (OR) Std. Error (OR) z value Pr(>|z|)
(Intercept) 1.039111 0.183840 0.217 0.828325
age 1.011327 0.003088 3.689 0.000225 ***
racewhite 1.907038 0.256462 4.800 1.58e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 2266.7 on 1999 degrees of freedom
Residual deviance: 2228.8 on 1997 degrees of freedom
AIC: 2234.8
Number of Fisher Scoring iterations: 4
MCMClogit iteration 1 of 11000
beta =
0.00210
0.98533
Metropolis acceptance rate for beta = 1.00000
MCMClogit iteration 1101 of 11000
beta =
-0.01824
0.93814
Metropolis acceptance rate for beta = 0.52952
MCMClogit iteration 2201 of 11000
beta =
0.00475
0.95312
Metropolis acceptance rate for beta = 0.52158
MCMClogit iteration 3301 of 11000
beta =
-0.01742
0.98950
Metropolis acceptance rate for beta = 0.52802
MCMClogit iteration 4401 of 11000
beta =
-0.03785
0.98980
Metropolis acceptance rate for beta = 0.52602
MCMClogit iteration 5501 of 11000
beta =
-0.04772
0.92064
Metropolis acceptance rate for beta = 0.52209
MCMClogit iteration 6601 of 11000
beta =
0.08646
1.00539
Metropolis acceptance rate for beta = 0.52174
MCMClogit iteration 7701 of 11000
beta =
0.07100
1.01120
Metropolis acceptance rate for beta = 0.52578
MCMClogit iteration 8801 of 11000
beta =
-0.00420
0.88082
Metropolis acceptance rate for beta = 0.52460
MCMClogit iteration 9901 of 11000
beta =
-0.01473
0.87193
Metropolis acceptance rate for beta = 0.52227
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
The Metropolis acceptance rate for beta was 0.52218
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Patrick Lam. 2011.
normal-gee: General Estimating Equation for Normal Regression
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Patrick Lam. 2011.
normal-gee: General Estimating Equation for Normal Regression
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
Model: Combined Imputations
Estimate Std.Error z value Pr(>|z|)
(Intercept) 34.66099 2.83523 12.23 <2e-16
cyl -1.58728 0.49875 -3.18 0.0015
disp -0.02058 0.00696 -2.96 0.0031
For results from individual imputed datasets, use summary(x, subset = i:j)
Next step: Use 'setx' method
MCMCregress iteration 1 of 11000
beta =
0.01119
1.03455
sigma2 = 1.00941
MCMCregress iteration 1101 of 11000
beta =
-0.02594
1.06744
sigma2 = 1.03889
MCMCregress iteration 2201 of 11000
beta =
-0.05222
0.97402
sigma2 = 1.01563
MCMCregress iteration 3301 of 11000
beta =
-0.05933
0.97920
sigma2 = 1.00108
MCMCregress iteration 4401 of 11000
beta =
-0.01337
1.01322
sigma2 = 0.95198
MCMCregress iteration 5501 of 11000
beta =
-0.02224
1.04994
sigma2 = 0.99842
MCMCregress iteration 6601 of 11000
beta =
-0.02043
1.00096
sigma2 = 0.93645
MCMCregress iteration 7701 of 11000
beta =
0.01190
1.06590
sigma2 = 1.01251
MCMCregress iteration 8801 of 11000
beta =
-0.01996
1.01412
sigma2 = 0.95430
MCMCregress iteration 9901 of 11000
beta =
-0.03583
0.97622
sigma2 = 0.99748
How to cite this model in Zelig:
R Core Team. 2007.
poisson: Poisson Regression for Event Count Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
poisson: Poisson Regression for Event Count Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
MCMCpoisson iteration 1 of 11000
beta =
-0.01862
1.02535
Metropolis acceptance rate for beta = 1.00000
MCMCpoisson iteration 1101 of 11000
beta =
-0.00207
1.01122
Metropolis acceptance rate for beta = 0.53224
MCMCpoisson iteration 2201 of 11000
beta =
-0.02369
1.02072
Metropolis acceptance rate for beta = 0.51613
MCMCpoisson iteration 3301 of 11000
beta =
-0.01912
1.02585
Metropolis acceptance rate for beta = 0.51712
MCMCpoisson iteration 4401 of 11000
beta =
-0.05394
1.03940
Metropolis acceptance rate for beta = 0.51602
MCMCpoisson iteration 5501 of 11000
beta =
-0.04054
1.02780
Metropolis acceptance rate for beta = 0.51736
MCMCpoisson iteration 6601 of 11000
beta =
0.08526
0.96386
Metropolis acceptance rate for beta = 0.51750
MCMCpoisson iteration 7701 of 11000
beta =
0.03033
0.99266
Metropolis acceptance rate for beta = 0.52305
MCMCpoisson iteration 8801 of 11000
beta =
-0.00782
1.01038
Metropolis acceptance rate for beta = 0.52324
MCMCpoisson iteration 9901 of 11000
beta =
-0.00920
1.01052
Metropolis acceptance rate for beta = 0.52096
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
The Metropolis acceptance rate for beta was 0.51927
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
How to cite this model in Zelig:
R Core Team. 2007.
probit: Probit Regression for Dichotomous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
MCMCprobit iteration 1 of 11000
beta =
-0.01062
0.93045
MCMCprobit iteration 1101 of 11000
beta =
0.00854
0.98889
MCMCprobit iteration 2201 of 11000
beta =
-0.05915
1.05668
MCMCprobit iteration 3301 of 11000
beta =
-0.01819
0.86660
MCMCprobit iteration 4401 of 11000
beta =
-0.01958
0.95408
MCMCprobit iteration 5501 of 11000
beta =
-0.04281
0.93104
MCMCprobit iteration 6601 of 11000
beta =
-0.04593
0.95252
MCMCprobit iteration 7701 of 11000
beta =
0.01012
1.01175
MCMCprobit iteration 8801 of 11000
beta =
0.01985
1.03090
MCMCprobit iteration 9901 of 11000
beta =
-0.02902
0.99117
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
-- Imputation 1 --
1 2
-- Imputation 2 --
1 2
-- Imputation 3 --
1 2 3
-- Imputation 4 --
1 2 3
-- Imputation 5 --
1 2 3
How to cite this model in Zelig:
Alexander D'Amour. 2008.
quantile: Quantile Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau. 2022.
relogit: Rare Events Logistic Regression for Dichotomous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau. 2022.
relogit: Rare Events Logistic Regression for Dichotomous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau. 2022.
relogit: Rare Events Logistic Regression for Dichotomous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau. 2022.
relogit: Rare Events Logistic Regression for Dichotomous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
Model:
Call:
relogit(formula = cbind(conflict, 1 - conflict) ~ major + contig +
power + maxdem + mindem + years, data = as.data.frame(.),
tau = 0.00343020423212146, bias.correct = TRUE, case.control = "weighting")
Deviance Residuals:
Min 1Q Median 3Q Max
-0.94933 -0.04958 -0.02173 0.19019 0.48253
Coefficients:
Estimate Std. Error (robust) z value Pr(>|z|)
(Intercept) -6.61889 0.31748 -20.848 < 2e-16 ***
major 1.67218 0.27842 6.006 1.9e-09 ***
contig 4.01640 0.22954 17.498 < 2e-16 ***
power 0.28836 0.41574 0.694 0.487925
maxdem 0.06629 0.01925 3.444 0.000573 ***
mindem -0.08143 0.02996 -2.718 0.006572 **
years -0.11707 0.01336 -8.764 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 143.116 on 3125 degrees of freedom
Residual deviance: 91.178 on 3119 degrees of freedom
AIC: 27.041
Number of Fisher Scoring iterations: 10
Model:
Call:
relogit(formula = cbind(conflict, 1 - conflict) ~ major + contig +
power + maxdem + mindem + years, data = as.data.frame(.),
tau = 0.00343020423212146, bias.correct = TRUE, case.control = "weighting")
Deviance Residuals:
Min 1Q Median 3Q Max
-0.94933 -0.04958 -0.02173 0.19019 0.48253
Coefficients:
Estimate Std. Error (robust) z value Pr(>|z|)
(Intercept) -6.61889 0.31748 -20.848 < 2e-16 ***
major 1.67218 0.27842 6.006 1.9e-09 ***
contig 4.01640 0.22954 17.498 < 2e-16 ***
power 0.28836 0.41574 0.694 0.487925
maxdem 0.06629 0.01925 3.444 0.000573 ***
mindem -0.08143 0.02996 -2.718 0.006572 **
years -0.11707 0.01336 -8.764 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 143.116 on 3125 degrees of freedom
Residual deviance: 91.178 on 3119 degrees of freedom
AIC: 27.041
Number of Fisher Scoring iterations: 10
Model:
Call:
relogit(formula = cbind(conflict, 1 - conflict) ~ major + contig +
power + maxdem + mindem + years, data = as.data.frame(.),
tau = 0.00343020423212146, bias.correct = TRUE, case.control = "weighting")
Deviance Residuals:
Min 1Q Median 3Q Max
-0.94933 -0.04958 -0.02173 0.19019 0.48253
Coefficients:
Estimate (OR) Std. Error (OR, robust) z value Pr(>|z|)
(Intercept) 0.001335 0.001521 -20.848 < 2e-16 ***
major 5.323748 4.119262 6.006 1.9e-09 ***
contig 55.501077 41.045844 17.498 < 2e-16 ***
power 1.334239 1.410153 0.694 0.487925
maxdem 1.068533 0.054366 3.444 0.000573 ***
mindem 0.921799 0.067683 -2.718 0.006572 **
years 0.889520 0.041892 -8.764 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 143.116 on 3125 degrees of freedom
Residual deviance: 91.178 on 3119 degrees of freedom
AIC: 27.041
Number of Fisher Scoring iterations: 10
Model:
Call:
z5$zelig(formula = conflict ~ major + contig + power + maxdem +
mindem + years, tau = 1042/303772, case.control = "prior",
data = mid)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.0596 -0.0376 -0.0231 2.1085 4.4649
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -7.525688 0.179685 -41.883 < 2e-16
major 2.433432 0.157561 15.444 < 2e-16
contig 4.112491 0.157650 26.086 < 2e-16
power 1.053747 0.217243 4.851 1.23e-06
maxdem 0.048431 0.010065 4.812 1.50e-06
mindem -0.065249 0.012802 -5.097 3.45e-07
years -0.063359 0.005705 -11.106 < 2e-16
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 3979.5 on 3125 degrees of freedom
Residual deviance: 1868.5 on 3119 degrees of freedom
AIC: 1882.5
Number of Fisher Scoring iterations: 6
Next step: Use 'setx' method
Model:
Call:
`z5$zelig`(formula = conflict ~ major + contig + power + maxdem +
mindem + years, tau = 1042/303772, case.control = "prior",
data = mid)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.0596 -0.0376 -0.0231 2.1085 4.4649
Coefficients:
Estimate (OR) Std. Error (OR) z value Pr(>|z|)
(Intercept) 5.391e-04 9.686e-05 -41.883 < 2e-16 ***
major 1.140e+01 1.796e+00 15.444 < 2e-16 ***
contig 6.110e+01 9.632e+00 26.086 < 2e-16 ***
power 2.868e+00 6.231e-01 4.851 1.23e-06 ***
maxdem 1.050e+00 1.056e-02 4.812 1.50e-06 ***
mindem 9.368e-01 1.199e-02 -5.097 3.45e-07 ***
years 9.386e-01 5.355e-03 -11.106 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 3979.5 on 3125 degrees of freedom
Residual deviance: 1868.5 on 3119 degrees of freedom
AIC: 1882.5
Number of Fisher Scoring iterations: 6
How to cite this model in Zelig:
Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau. 2022.
relogit: Rare Events Logistic Regression for Dichotomous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
MCMCtobit iteration 1 of 11000
beta =
0.09918
0.93061
sigma2 = 0.70553
MCMCtobit iteration 1101 of 11000
beta =
0.07445
0.91668
sigma2 = 0.99194
MCMCtobit iteration 2201 of 11000
beta =
0.00068
0.99780
sigma2 = 0.99760
MCMCtobit iteration 3301 of 11000
beta =
0.04504
0.95894
sigma2 = 0.95844
MCMCtobit iteration 4401 of 11000
beta =
-0.03116
1.00165
sigma2 = 0.97216
MCMCtobit iteration 5501 of 11000
beta =
-0.01771
0.98161
sigma2 = 0.94482
MCMCtobit iteration 6601 of 11000
beta =
-0.00829
0.96500
sigma2 = 0.90195
MCMCtobit iteration 7701 of 11000
beta =
0.09984
0.93619
sigma2 = 1.03124
MCMCtobit iteration 8801 of 11000
beta =
0.03092
0.99268
sigma2 = 0.95955
MCMCtobit iteration 9901 of 11000
beta =
0.04477
0.93956
sigma2 = 1.01930
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
Noninteger weights were set, but the model in Zelig is only able to use integer valued weights.
A bootstrapped version of the dataset was constructed using the weights as sample probabilities.
How to cite this model in Zelig:
R Core Team. 2007.
logit: Logistic Regression for Dichotomous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
logit: Logistic Regression for Dichotomous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
poisson: Poisson Regression for Event Count Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
poisson: Poisson Regression for Event Count Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
setx:
(Intercept) x
1 1 0
setx1:
(Intercept) x
1 1 1
Next step: Use 'sim' method
sim x :
-----
ev
mean sd 50% 2.5% 97.5%
1 -0.000499563 0.006496101 -0.0005420348 -0.01340449 0.01259395
pv
mean sd 50% 2.5% 97.5%
[1,] -0.002512827 0.09819415 -0.003183565 -0.2043797 0.1944359
sim x1 :
-----
ev
mean sd 50% 2.5% 97.5%
1 1.00022 0.006045548 1.000062 0.9887536 1.01215
pv
mean sd 50% 2.5% 97.5%
[1,] 0.9993027 0.09953018 0.9999023 0.8025547 1.197061
fd
mean sd 50% 2.5% 97.5%
1 1.000719 0.01076163 1.000655 0.9799646 1.022849
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
Model: Combined Bootstraps
Estimate Std.Error z value Pr(>|z|)
(Intercept) -0.0003 0.0060 -0.05 0.96
x 1.0006 0.0115 86.67 <2e-16
For results from individual bootstrapped datasets, use summary(x, subset = i:j)
Next step: Use 'setx' method
Bootstrapped Dataset 2
Call:
z5$zelig(formula = formula, data = data, by = by, bootstrap = 20)
Residuals:
Min 1Q Median 3Q Max
-0.09973 -0.09750 -0.09523 0.10254 0.10486
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.004872 0.006185 -0.788 0.431
x 1.004608 0.010780 93.194 <2e-16
Residual standard error: 0.1001 on 998 degrees of freedom
Multiple R-squared: 0.8969, Adjusted R-squared: 0.8968
F-statistic: 8685 on 1 and 998 DF, p-value: < 2.2e-16
Bootstrapped Dataset 3
Call:
z5$zelig(formula = formula, data = data, by = by, bootstrap = 20)
Residuals:
Min 1Q Median 3Q Max
-0.10360 -0.09514 -0.08812 0.10411 0.11242
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.003619 0.006352 0.57 0.569
x 0.983959 0.010756 91.48 <2e-16
Residual standard error: 0.09988 on 998 degrees of freedom
Multiple R-squared: 0.8934, Adjusted R-squared: 0.8933
F-statistic: 8368 on 1 and 998 DF, p-value: < 2.2e-16
Next step: Use 'setx' method
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
Model: Combined Imputations
Estimate Std.Error z value Pr(>|z|)
(Intercept) -1.001 1.733 -0.58 0.56
x 1.001 0.011 91.25 <2e-16
For results from individual imputed datasets, use summary(x, subset = i:j)
Next step: Use 'setx' method
Imputed Dataset 1
Call:
z5$zelig(formula = formula, data = data, by = by)
Residuals:
Min 1Q Median 3Q Max
-0.1003 -0.1000 0.0000 0.1000 0.1003
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.0003003 0.0063356 -0.047 0.962
x 1.0006000 0.0109654 91.251 <2e-16
Residual standard error: 0.1001 on 998 degrees of freedom
Multiple R-squared: 0.893, Adjusted R-squared: 0.8929
F-statistic: 8327 on 1 and 998 DF, p-value: < 2.2e-16
Next step: Use 'setx' method
Model:
$rr
[1] 0
Call:
zb.out$zelig(formula = yb ~ xx + zz, data = data, by = "rr")
Deviance Residuals:
Min 1Q Median 3Q Max
-2.6031 0.2531 0.3125 0.3947 0.6564
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.274 1.045 1.219 0.223
xx 1.088 1.195 0.910 0.363
zz 1.975 2.152 0.918 0.359
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 28.820 on 55 degrees of freedom
Residual deviance: 26.896 on 53 degrees of freedom
AIC: 32.896
Number of Fisher Scoring iterations: 6
Model:
$rr
[1] 1
Call:
zb.out$zelig(formula = yb ~ xx + zz, data = data, by = "rr")
Deviance Residuals:
Min 1Q Median 3Q Max
-2.6982 0.2266 0.3859 0.7298 1.0780
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.2255 1.0561 -0.214 0.831
xx 2.2702 1.1632 1.952 0.051
zz 2.1285 1.8878 1.128 0.260
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 41.724 on 43 degrees of freedom
Residual deviance: 35.553 on 41 degrees of freedom
AIC: 41.553
Number of Fisher Scoring iterations: 5
Next step: Use 'setx' method
sim x :
-----
ev
mean sd 50% 2.5% 97.5%
[1,] 0.9476733 0.05386777 0.9665833 0.8009766 0.9955033
pv
0 1
[1,] 0.058 0.942
sim x :
-----
ev
mean sd 50% 2.5% 97.5%
[1,] 0.9367684 0.07021485 0.9611249 0.7428204 0.9949634
pv
0 1
[1,] 0.061 0.939
sim x1 :
-----
ev
mean sd 50% 2.5% 97.5%
[1,] 0.8918403 0.06422275 0.9049403 0.7298034 0.9740786
pv
0 1
[1,] 0.101 0.899
fd
mean sd 50% 2.5% 97.5%
[1,] -0.05583303 0.07977401 -0.05088306 -0.2221262 0.1014462
sim x1 :
-----
ev
mean sd 50% 2.5% 97.5%
[1,] 0.6990619 0.09467077 0.7081549 0.5028567 0.8603348
pv
0 1
[1,] 0.31 0.69
fd
mean sd 50% 2.5% 97.5%
[1,] -0.2377065 0.1148547 -0.2380952 -0.466211 -0.01659926
[ FAIL 1 | WARN 13 | SKIP 17 | PASS 212 ]
== Skipped tests ===============================================================
* On CRAN (17)
== Failed tests ================================================================
-- Error (test-interface.R:74:5): REQUIRE TEST zelig_qi_to_df multinomial outcome --
Error in `if (mcmc.method == "RWM") {
auto.Scythe.call(output.object = "posterior", cc.fun.name = "MCMCmnlMH",
sample.nonconst = sample, Y = Y, X = X, burnin = as.integer(burnin),
mcmc = as.integer(mcmc), thin = as.integer(thin), tune = tune,
lecuyer = as.integer(lecuyer), seedarray = as.integer(seed.array),
lecuyerstream = as.integer(lecuyer.stream), verbose = as.integer(verbose),
betastart = beta.start, betamode = beta.mode, b0 = b0,
B0 = B0, V = V, RW = as.integer(1), tdf = as.double(tdf))
output <- form.mcmc.object(posterior, names = xnames, title = "MCMCmnl Posterior Sample")
} else if (mcmc.method == "IndMH") {
auto.Scythe.call(output.object = "posterior", cc.fun.name = "MCMCmnlMH",
sample.nonconst = sample, Y = Y, X = X, burnin = as.integer(burnin),
mcmc = as.integer(mcmc), thin = as.integer(thin), tune = tune,
lecuyer = as.integer(lecuyer), seedarray = as.integer(seed.array),
lecuyerstream = as.integer(lecuyer.stream), verbose = as.integer(verbose),
betastart = beta.start, betamode = beta.mode, b0 = b0,
B0 = B0, V = V, RW = as.integer(0), tdf = as.double(tdf))
output <- form.mcmc.object(posterior, names = xnames, title = "MCMCmnl Posterior Sample")
} else if (mcmc.method == "slice") {
auto.Scythe.call(output.object = "posterior", cc.fun.name = "MCMCmnlslice",
sample.nonconst = sample, Y = Y, X = X, burnin = as.integer(burnin),
mcmc = as.integer(mcmc), thin = as.integer(thin), lecuyer = as.integer(lecuyer),
seedarray = as.integer(seed.array), lecuyerstream = as.integer(lecuyer.stream),
verbose = as.integer(verbose), betastart = beta.start,
b0 = b0, B0 = B0, V = V)
output <- form.mcmc.object(posterior, names = xnames, title = "MCMCmnl Posterior Sample")
} else {
cat("\n\nmcmc.method not equal to one of 'RWM', 'IndMH', or 'slice'.\n")
stop("Please respecifify and call MCMCmnl() again.\n")
}`: the condition has length > 1
Backtrace:
x
1. +-... %>% zelig_qi_to_df() at test-interface.R:74:4
2. +-Zelig::zelig_qi_to_df(.)
3. | \-Zelig::is_zelig(obj)
4. +-Zelig::sim(.)
5. | \-Zelig::is_zelig(obj)
6. +-Zelig::setx(.)
7. | \-Zelig::is_zelig(obj, fail = FALSE)
8. +-Zelig::zelig(...)
9. | \-z5$zelig(formula = formula, data = data, ..., by = by)
10. | \-Zelig callSuper(formula = formula, data = data, ..., by = by, bootstrap = FALSE)
11. | \-.self$data %>% group_by_(.self$by) %>% ...
12. +-dplyr::do(., z.out = eval(fn2(.self$model.call, quote(as.data.frame(.)))))
13. +-dplyr:::do.grouped_df(., z.out = eval(fn2(.self$model.call, quote(as.data.frame(.)))))
14. | \-rlang::eval_tidy(args[[j]], mask)
15. \-base::eval(fn2(.self$model.call, quote(as.data.frame(.))))
16. \-base::eval(fn2(.self$model.call, quote(as.data.frame(.))))
17. \-MCMCpack::MCMCmnl(...)
[ FAIL 1 | WARN 13 | SKIP 17 | PASS 212 ]
Error: Test failures
Execution halted
Flavor: r-devel-linux-x86_64-debian-clang
Version: 5.1.7
Check: tests
Result: ERROR
Running ‘testthat.R’ [155s/239s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(AER)
Loading required package: car
Loading required package: carData
Loading required package: lmtest
Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, as.Date.numeric
Loading required package: sandwich
Loading required package: survival
> library(dplyr)
Attaching package: 'dplyr'
The following object is masked from 'package:car':
recode
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
> library(geepack)
> library(survey)
Loading required package: grid
Loading required package: Matrix
Attaching package: 'survey'
The following object is masked from 'package:graphics':
dotchart
> library(testthat)
Attaching package: 'testthat'
The following object is masked from 'package:dplyr':
matches
>
> set.seed(123)
> test_check("Zelig")
Loading required package: Zelig
-- Imputation 1 --
1 2 3
-- Imputation 2 --
1 2 3
-- Imputation 3 --
1 2 3
-- Imputation 4 --
1 2 3
-- Imputation 5 --
1 2 3
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
-- Imputation 1 --
1 2
-- Imputation 2 --
1 2 3
-- Imputation 3 --
1 2
-- Imputation 4 --
1 2 3
-- Imputation 5 --
1 2
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Ben Goodrich, and Ying Lu. 2013.
normal-bayes: Bayesian Normal Linear Regression
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Andrew D. Martin, Kevin M. Quinn, and Jong Hee Park. 2013.
factor-bayes: Bayesian Factor Analysis
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Terry M. Therneau, and Thomas Lumley. 2011.
exp: Exponential Regression for Duration Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
gamma: Gamma Regression for Continuous, Positive Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
Calculating MLEs and large sample var-cov matrix.
This may take a moment...
Inverting Hessian to get large sample var-cov matrix.
Calculating MLEs and large sample var-cov matrix.
This may take a moment...
Inverting Hessian to get large sample var-cov matrix.
How to cite this model in Zelig:
Christian Kleiber and Achim Zeileis. 2008.
ivreg: Instrumental-Variable Regression
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Christian Kleiber and Achim Zeileis. 2008.
ivreg: Instrumental-Variable Regression
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
Model:
Call:
stats::glm(formula = vote ~ age + race, family = binomial("logit"),
data = as.data.frame(.))
Deviance Residuals:
Min 1Q Median 3Q Max
-1.9268 -1.2962 0.7072 0.7766 1.0723
Coefficients:
Estimate (OR) Std. Error (OR) z value Pr(>|z|)
(Intercept) 1.039111 0.183840 0.217 0.828325
age 1.011327 0.003088 3.689 0.000225 ***
racewhite 1.907038 0.256462 4.800 1.58e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 2266.7 on 1999 degrees of freedom
Residual deviance: 2228.8 on 1997 degrees of freedom
AIC: 2234.8
Number of Fisher Scoring iterations: 4
MCMClogit iteration 1 of 11000
beta =
0.00210
0.98533
Metropolis acceptance rate for beta = 1.00000
MCMClogit iteration 1101 of 11000
beta =
-0.01824
0.93814
Metropolis acceptance rate for beta = 0.52952
MCMClogit iteration 2201 of 11000
beta =
0.00475
0.95312
Metropolis acceptance rate for beta = 0.52158
MCMClogit iteration 3301 of 11000
beta =
-0.01742
0.98950
Metropolis acceptance rate for beta = 0.52802
MCMClogit iteration 4401 of 11000
beta =
-0.03785
0.98980
Metropolis acceptance rate for beta = 0.52602
MCMClogit iteration 5501 of 11000
beta =
-0.04772
0.92064
Metropolis acceptance rate for beta = 0.52209
MCMClogit iteration 6601 of 11000
beta =
0.08646
1.00539
Metropolis acceptance rate for beta = 0.52174
MCMClogit iteration 7701 of 11000
beta =
0.07100
1.01120
Metropolis acceptance rate for beta = 0.52578
MCMClogit iteration 8801 of 11000
beta =
-0.00420
0.88082
Metropolis acceptance rate for beta = 0.52460
MCMClogit iteration 9901 of 11000
beta =
-0.01473
0.87193
Metropolis acceptance rate for beta = 0.52227
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
The Metropolis acceptance rate for beta was 0.52218
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Patrick Lam. 2011.
normal-gee: General Estimating Equation for Normal Regression
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Patrick Lam. 2011.
normal-gee: General Estimating Equation for Normal Regression
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
Model: Combined Imputations
Estimate Std.Error z value Pr(>|z|)
(Intercept) 34.66099 2.83523 12.23 <2e-16
cyl -1.58728 0.49875 -3.18 0.0015
disp -0.02058 0.00696 -2.96 0.0031
For results from individual imputed datasets, use summary(x, subset = i:j)
Next step: Use 'setx' method
MCMCregress iteration 1 of 11000
beta =
0.01119
1.03455
sigma2 = 1.00941
MCMCregress iteration 1101 of 11000
beta =
-0.02594
1.06744
sigma2 = 1.03889
MCMCregress iteration 2201 of 11000
beta =
-0.05222
0.97402
sigma2 = 1.01563
MCMCregress iteration 3301 of 11000
beta =
-0.05933
0.97920
sigma2 = 1.00108
MCMCregress iteration 4401 of 11000
beta =
-0.01337
1.01322
sigma2 = 0.95198
MCMCregress iteration 5501 of 11000
beta =
-0.02224
1.04994
sigma2 = 0.99842
MCMCregress iteration 6601 of 11000
beta =
-0.02043
1.00096
sigma2 = 0.93645
MCMCregress iteration 7701 of 11000
beta =
0.01190
1.06590
sigma2 = 1.01251
MCMCregress iteration 8801 of 11000
beta =
-0.01996
1.01412
sigma2 = 0.95430
MCMCregress iteration 9901 of 11000
beta =
-0.03583
0.97622
sigma2 = 0.99748
How to cite this model in Zelig:
R Core Team. 2007.
poisson: Poisson Regression for Event Count Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
poisson: Poisson Regression for Event Count Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
MCMCpoisson iteration 1 of 11000
beta =
-0.01862
1.02535
Metropolis acceptance rate for beta = 1.00000
MCMCpoisson iteration 1101 of 11000
beta =
-0.00207
1.01122
Metropolis acceptance rate for beta = 0.53224
MCMCpoisson iteration 2201 of 11000
beta =
-0.02369
1.02072
Metropolis acceptance rate for beta = 0.51613
MCMCpoisson iteration 3301 of 11000
beta =
-0.01912
1.02585
Metropolis acceptance rate for beta = 0.51712
MCMCpoisson iteration 4401 of 11000
beta =
-0.05394
1.03940
Metropolis acceptance rate for beta = 0.51602
MCMCpoisson iteration 5501 of 11000
beta =
-0.04054
1.02780
Metropolis acceptance rate for beta = 0.51736
MCMCpoisson iteration 6601 of 11000
beta =
0.08526
0.96386
Metropolis acceptance rate for beta = 0.51750
MCMCpoisson iteration 7701 of 11000
beta =
0.03033
0.99266
Metropolis acceptance rate for beta = 0.52305
MCMCpoisson iteration 8801 of 11000
beta =
-0.00782
1.01038
Metropolis acceptance rate for beta = 0.52324
MCMCpoisson iteration 9901 of 11000
beta =
-0.00920
1.01052
Metropolis acceptance rate for beta = 0.52096
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
The Metropolis acceptance rate for beta was 0.51927
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
How to cite this model in Zelig:
R Core Team. 2007.
probit: Probit Regression for Dichotomous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
MCMCprobit iteration 1 of 11000
beta =
-0.01062
0.93045
MCMCprobit iteration 1101 of 11000
beta =
0.00854
0.98889
MCMCprobit iteration 2201 of 11000
beta =
-0.05915
1.05668
MCMCprobit iteration 3301 of 11000
beta =
-0.01819
0.86660
MCMCprobit iteration 4401 of 11000
beta =
-0.01958
0.95408
MCMCprobit iteration 5501 of 11000
beta =
-0.04281
0.93104
MCMCprobit iteration 6601 of 11000
beta =
-0.04593
0.95252
MCMCprobit iteration 7701 of 11000
beta =
0.01012
1.01175
MCMCprobit iteration 8801 of 11000
beta =
0.01985
1.03090
MCMCprobit iteration 9901 of 11000
beta =
-0.02902
0.99117
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
-- Imputation 1 --
1 2
-- Imputation 2 --
1 2
-- Imputation 3 --
1 2 3
-- Imputation 4 --
1 2 3
-- Imputation 5 --
1 2 3
How to cite this model in Zelig:
Alexander D'Amour. 2008.
quantile: Quantile Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau. 2022.
relogit: Rare Events Logistic Regression for Dichotomous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau. 2022.
relogit: Rare Events Logistic Regression for Dichotomous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau. 2022.
relogit: Rare Events Logistic Regression for Dichotomous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau. 2022.
relogit: Rare Events Logistic Regression for Dichotomous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
Model:
Call:
relogit(formula = cbind(conflict, 1 - conflict) ~ major + contig +
power + maxdem + mindem + years, data = as.data.frame(.),
tau = 0.00343020423212146, bias.correct = TRUE, case.control = "weighting")
Deviance Residuals:
Min 1Q Median 3Q Max
-0.94933 -0.04958 -0.02173 0.19019 0.48253
Coefficients:
Estimate Std. Error (robust) z value Pr(>|z|)
(Intercept) -6.61889 0.31748 -20.848 < 2e-16 ***
major 1.67218 0.27842 6.006 1.9e-09 ***
contig 4.01640 0.22954 17.498 < 2e-16 ***
power 0.28836 0.41574 0.694 0.487925
maxdem 0.06629 0.01925 3.444 0.000573 ***
mindem -0.08143 0.02996 -2.718 0.006572 **
years -0.11707 0.01336 -8.764 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 143.116 on 3125 degrees of freedom
Residual deviance: 91.178 on 3119 degrees of freedom
AIC: 27.041
Number of Fisher Scoring iterations: 10
Model:
Call:
relogit(formula = cbind(conflict, 1 - conflict) ~ major + contig +
power + maxdem + mindem + years, data = as.data.frame(.),
tau = 0.00343020423212146, bias.correct = TRUE, case.control = "weighting")
Deviance Residuals:
Min 1Q Median 3Q Max
-0.94933 -0.04958 -0.02173 0.19019 0.48253
Coefficients:
Estimate Std. Error (robust) z value Pr(>|z|)
(Intercept) -6.61889 0.31748 -20.848 < 2e-16 ***
major 1.67218 0.27842 6.006 1.9e-09 ***
contig 4.01640 0.22954 17.498 < 2e-16 ***
power 0.28836 0.41574 0.694 0.487925
maxdem 0.06629 0.01925 3.444 0.000573 ***
mindem -0.08143 0.02996 -2.718 0.006572 **
years -0.11707 0.01336 -8.764 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 143.116 on 3125 degrees of freedom
Residual deviance: 91.178 on 3119 degrees of freedom
AIC: 27.041
Number of Fisher Scoring iterations: 10
Model:
Call:
relogit(formula = cbind(conflict, 1 - conflict) ~ major + contig +
power + maxdem + mindem + years, data = as.data.frame(.),
tau = 0.00343020423212146, bias.correct = TRUE, case.control = "weighting")
Deviance Residuals:
Min 1Q Median 3Q Max
-0.94933 -0.04958 -0.02173 0.19019 0.48253
Coefficients:
Estimate (OR) Std. Error (OR, robust) z value Pr(>|z|)
(Intercept) 0.001335 0.001521 -20.848 < 2e-16 ***
major 5.323748 4.119262 6.006 1.9e-09 ***
contig 55.501077 41.045844 17.498 < 2e-16 ***
power 1.334239 1.410153 0.694 0.487925
maxdem 1.068533 0.054366 3.444 0.000573 ***
mindem 0.921799 0.067683 -2.718 0.006572 **
years 0.889520 0.041892 -8.764 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 143.116 on 3125 degrees of freedom
Residual deviance: 91.178 on 3119 degrees of freedom
AIC: 27.041
Number of Fisher Scoring iterations: 10
Model:
Call:
z5$zelig(formula = conflict ~ major + contig + power + maxdem +
mindem + years, tau = 1042/303772, case.control = "prior",
data = mid)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.0596 -0.0376 -0.0231 2.1085 4.4649
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -7.525688 0.179685 -41.883 < 2e-16
major 2.433432 0.157561 15.444 < 2e-16
contig 4.112491 0.157650 26.086 < 2e-16
power 1.053747 0.217243 4.851 1.23e-06
maxdem 0.048431 0.010065 4.812 1.50e-06
mindem -0.065249 0.012802 -5.097 3.45e-07
years -0.063359 0.005705 -11.106 < 2e-16
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 3979.5 on 3125 degrees of freedom
Residual deviance: 1868.5 on 3119 degrees of freedom
AIC: 1882.5
Number of Fisher Scoring iterations: 6
Next step: Use 'setx' method
Model:
Call:
`z5$zelig`(formula = conflict ~ major + contig + power + maxdem +
mindem + years, tau = 1042/303772, case.control = "prior",
data = mid)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.0596 -0.0376 -0.0231 2.1085 4.4649
Coefficients:
Estimate (OR) Std. Error (OR) z value Pr(>|z|)
(Intercept) 5.391e-04 9.686e-05 -41.883 < 2e-16 ***
major 1.140e+01 1.796e+00 15.444 < 2e-16 ***
contig 6.110e+01 9.632e+00 26.086 < 2e-16 ***
power 2.868e+00 6.231e-01 4.851 1.23e-06 ***
maxdem 1.050e+00 1.056e-02 4.812 1.50e-06 ***
mindem 9.368e-01 1.199e-02 -5.097 3.45e-07 ***
years 9.386e-01 5.355e-03 -11.106 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 3979.5 on 3125 degrees of freedom
Residual deviance: 1868.5 on 3119 degrees of freedom
AIC: 1882.5
Number of Fisher Scoring iterations: 6
How to cite this model in Zelig:
Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau. 2022.
relogit: Rare Events Logistic Regression for Dichotomous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
MCMCtobit iteration 1 of 11000
beta =
0.09918
0.93061
sigma2 = 0.70553
MCMCtobit iteration 1101 of 11000
beta =
0.07445
0.91668
sigma2 = 0.99194
MCMCtobit iteration 2201 of 11000
beta =
0.00068
0.99780
sigma2 = 0.99760
MCMCtobit iteration 3301 of 11000
beta =
0.04504
0.95894
sigma2 = 0.95844
MCMCtobit iteration 4401 of 11000
beta =
-0.03116
1.00165
sigma2 = 0.97216
MCMCtobit iteration 5501 of 11000
beta =
-0.01771
0.98161
sigma2 = 0.94482
MCMCtobit iteration 6601 of 11000
beta =
-0.00829
0.96500
sigma2 = 0.90195
MCMCtobit iteration 7701 of 11000
beta =
0.09984
0.93619
sigma2 = 1.03124
MCMCtobit iteration 8801 of 11000
beta =
0.03092
0.99268
sigma2 = 0.95955
MCMCtobit iteration 9901 of 11000
beta =
0.04477
0.93956
sigma2 = 1.01930
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
Noninteger weights were set, but the model in Zelig is only able to use integer valued weights.
A bootstrapped version of the dataset was constructed using the weights as sample probabilities.
How to cite this model in Zelig:
R Core Team. 2007.
logit: Logistic Regression for Dichotomous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
logit: Logistic Regression for Dichotomous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
poisson: Poisson Regression for Event Count Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
poisson: Poisson Regression for Event Count Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
setx:
(Intercept) x
1 1 0
setx1:
(Intercept) x
1 1 1
Next step: Use 'sim' method
sim x :
-----
ev
mean sd 50% 2.5% 97.5%
1 -0.000499563 0.006496101 -0.0005420348 -0.01340449 0.01259395
pv
mean sd 50% 2.5% 97.5%
[1,] -0.002512827 0.09819415 -0.003183565 -0.2043797 0.1944359
sim x1 :
-----
ev
mean sd 50% 2.5% 97.5%
1 1.00022 0.006045548 1.000062 0.9887536 1.01215
pv
mean sd 50% 2.5% 97.5%
[1,] 0.9993027 0.09953018 0.9999023 0.8025547 1.197061
fd
mean sd 50% 2.5% 97.5%
1 1.000719 0.01076163 1.000655 0.9799646 1.022849
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
Model: Combined Bootstraps
Estimate Std.Error z value Pr(>|z|)
(Intercept) -0.0003 0.0060 -0.05 0.96
x 1.0006 0.0115 86.67 <2e-16
For results from individual bootstrapped datasets, use summary(x, subset = i:j)
Next step: Use 'setx' method
Bootstrapped Dataset 2
Call:
z5$zelig(formula = formula, data = data, by = by, bootstrap = 20)
Residuals:
Min 1Q Median 3Q Max
-0.09973 -0.09750 -0.09523 0.10254 0.10486
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.004872 0.006185 -0.788 0.431
x 1.004608 0.010780 93.194 <2e-16
Residual standard error: 0.1001 on 998 degrees of freedom
Multiple R-squared: 0.8969, Adjusted R-squared: 0.8968
F-statistic: 8685 on 1 and 998 DF, p-value: < 2.2e-16
Bootstrapped Dataset 3
Call:
z5$zelig(formula = formula, data = data, by = by, bootstrap = 20)
Residuals:
Min 1Q Median 3Q Max
-0.10360 -0.09514 -0.08812 0.10411 0.11242
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.003619 0.006352 0.57 0.569
x 0.983959 0.010756 91.48 <2e-16
Residual standard error: 0.09988 on 998 degrees of freedom
Multiple R-squared: 0.8934, Adjusted R-squared: 0.8933
F-statistic: 8368 on 1 and 998 DF, p-value: < 2.2e-16
Next step: Use 'setx' method
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
Model: Combined Imputations
Estimate Std.Error z value Pr(>|z|)
(Intercept) -1.001 1.733 -0.58 0.56
x 1.001 0.011 91.25 <2e-16
For results from individual imputed datasets, use summary(x, subset = i:j)
Next step: Use 'setx' method
Imputed Dataset 1
Call:
z5$zelig(formula = formula, data = data, by = by)
Residuals:
Min 1Q Median 3Q Max
-0.1003 -0.1000 0.0000 0.1000 0.1003
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.0003003 0.0063356 -0.047 0.962
x 1.0006000 0.0109654 91.251 <2e-16
Residual standard error: 0.1001 on 998 degrees of freedom
Multiple R-squared: 0.893, Adjusted R-squared: 0.8929
F-statistic: 8327 on 1 and 998 DF, p-value: < 2.2e-16
Next step: Use 'setx' method
Model:
$rr
[1] 0
Call:
zb.out$zelig(formula = yb ~ xx + zz, data = data, by = "rr")
Deviance Residuals:
Min 1Q Median 3Q Max
-2.6031 0.2531 0.3125 0.3947 0.6564
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.274 1.045 1.219 0.223
xx 1.088 1.195 0.910 0.363
zz 1.975 2.152 0.918 0.359
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 28.820 on 55 degrees of freedom
Residual deviance: 26.896 on 53 degrees of freedom
AIC: 32.896
Number of Fisher Scoring iterations: 6
Model:
$rr
[1] 1
Call:
zb.out$zelig(formula = yb ~ xx + zz, data = data, by = "rr")
Deviance Residuals:
Min 1Q Median 3Q Max
-2.6982 0.2266 0.3859 0.7298 1.0780
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.2255 1.0561 -0.214 0.831
xx 2.2702 1.1632 1.952 0.051
zz 2.1285 1.8878 1.128 0.260
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 41.724 on 43 degrees of freedom
Residual deviance: 35.553 on 41 degrees of freedom
AIC: 41.553
Number of Fisher Scoring iterations: 5
Next step: Use 'setx' method
sim x :
-----
ev
mean sd 50% 2.5% 97.5%
[1,] 0.9476733 0.05386777 0.9665833 0.8009766 0.9955033
pv
0 1
[1,] 0.058 0.942
sim x :
-----
ev
mean sd 50% 2.5% 97.5%
[1,] 0.9367684 0.07021485 0.9611249 0.7428204 0.9949634
pv
0 1
[1,] 0.061 0.939
sim x1 :
-----
ev
mean sd 50% 2.5% 97.5%
[1,] 0.8918403 0.06422275 0.9049403 0.7298034 0.9740786
pv
0 1
[1,] 0.101 0.899
fd
mean sd 50% 2.5% 97.5%
[1,] -0.05583303 0.07977401 -0.05088306 -0.2221262 0.1014462
sim x1 :
-----
ev
mean sd 50% 2.5% 97.5%
[1,] 0.6990619 0.09467077 0.7081549 0.5028567 0.8603348
pv
0 1
[1,] 0.31 0.69
fd
mean sd 50% 2.5% 97.5%
[1,] -0.2377065 0.1148547 -0.2380952 -0.466211 -0.01659926
[ FAIL 1 | WARN 13 | SKIP 17 | PASS 212 ]
══ Skipped tests ═══════════════════════════════════════════════════════════════
• On CRAN (17)
══ Failed tests ════════════════════════════════════════════════════════════════
── Error (test-interface.R:74:5): REQUIRE TEST zelig_qi_to_df multinomial outcome ──
Error in `if (mcmc.method == "RWM") {
auto.Scythe.call(output.object = "posterior", cc.fun.name = "MCMCmnlMH",
sample.nonconst = sample, Y = Y, X = X, burnin = as.integer(burnin),
mcmc = as.integer(mcmc), thin = as.integer(thin), tune = tune,
lecuyer = as.integer(lecuyer), seedarray = as.integer(seed.array),
lecuyerstream = as.integer(lecuyer.stream), verbose = as.integer(verbose),
betastart = beta.start, betamode = beta.mode, b0 = b0,
B0 = B0, V = V, RW = as.integer(1), tdf = as.double(tdf))
output <- form.mcmc.object(posterior, names = xnames, title = "MCMCmnl Posterior Sample")
} else if (mcmc.method == "IndMH") {
auto.Scythe.call(output.object = "posterior", cc.fun.name = "MCMCmnlMH",
sample.nonconst = sample, Y = Y, X = X, burnin = as.integer(burnin),
mcmc = as.integer(mcmc), thin = as.integer(thin), tune = tune,
lecuyer = as.integer(lecuyer), seedarray = as.integer(seed.array),
lecuyerstream = as.integer(lecuyer.stream), verbose = as.integer(verbose),
betastart = beta.start, betamode = beta.mode, b0 = b0,
B0 = B0, V = V, RW = as.integer(0), tdf = as.double(tdf))
output <- form.mcmc.object(posterior, names = xnames, title = "MCMCmnl Posterior Sample")
} else if (mcmc.method == "slice") {
auto.Scythe.call(output.object = "posterior", cc.fun.name = "MCMCmnlslice",
sample.nonconst = sample, Y = Y, X = X, burnin = as.integer(burnin),
mcmc = as.integer(mcmc), thin = as.integer(thin), lecuyer = as.integer(lecuyer),
seedarray = as.integer(seed.array), lecuyerstream = as.integer(lecuyer.stream),
verbose = as.integer(verbose), betastart = beta.start,
b0 = b0, B0 = B0, V = V)
output <- form.mcmc.object(posterior, names = xnames, title = "MCMCmnl Posterior Sample")
} else {
cat("\n\nmcmc.method not equal to one of 'RWM', 'IndMH', or 'slice'.\n")
stop("Please respecifify and call MCMCmnl() again.\n")
}`: the condition has length > 1
Backtrace:
▆
1. ├─... %>% zelig_qi_to_df() at test-interface.R:74:4
2. ├─Zelig::zelig_qi_to_df(.)
3. │ └─Zelig::is_zelig(obj)
4. ├─Zelig::sim(.)
5. │ └─Zelig::is_zelig(obj)
6. ├─Zelig::setx(.)
7. │ └─Zelig::is_zelig(obj, fail = FALSE)
8. ├─Zelig::zelig(...)
9. │ └─z5$zelig(formula = formula, data = data, ..., by = by)
10. │ └─Zelig callSuper(formula = formula, data = data, ..., by = by, bootstrap = FALSE)
11. │ └─.self$data %>% group_by_(.self$by) %>% ...
12. ├─dplyr::do(., z.out = eval(fn2(.self$model.call, quote(as.data.frame(.)))))
13. ├─dplyr:::do.grouped_df(., z.out = eval(fn2(.self$model.call, quote(as.data.frame(.)))))
14. │ └─rlang::eval_tidy(args[[j]], mask)
15. └─base::eval(fn2(.self$model.call, quote(as.data.frame(.))))
16. └─base::eval(fn2(.self$model.call, quote(as.data.frame(.))))
17. └─MCMCpack::MCMCmnl(...)
[ FAIL 1 | WARN 13 | SKIP 17 | PASS 212 ]
Error: Test failures
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 5.1.7
Check: examples
Result: ERROR
Running examples in ‘Zelig-Ex.R’ failed
The error most likely occurred in:
> ### Name: Zelig-mlogit-bayes-class
> ### Title: Bayesian Multinomial Logistic Regression
> ### Aliases: Zelig-mlogit-bayes-class zmlogitbayes
>
> ### ** Examples
>
> data(mexico)
> z.out <- zelig(vote88 ~ pristr + othcok + othsocok,model = "mlogit.bayes",
+ data = mexico,verbose = FALSE)
Calculating MLEs and large sample var-cov matrix.
This may take a moment...
Inverting Hessian to get large sample var-cov matrix.
Calculating MLEs and large sample var-cov matrix.
This may take a moment...
Inverting Hessian to get large sample var-cov matrix.
Error in if (mcmc.method == "RWM") { : the condition has length > 1
Calls: zelig ... do.grouped_df -> eval_tidy -> eval -> eval -> <Anonymous>
Execution halted
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-x86_64-new-UL, r-devel-windows-x86_64-new-TK
Version: 5.1.7
Check: tests
Result: ERROR
Running ‘testthat.R’ [4m/10m]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(AER)
Loading required package: car
Loading required package: carData
Loading required package: lmtest
Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, as.Date.numeric
Loading required package: sandwich
Loading required package: survival
> library(dplyr)
Attaching package: 'dplyr'
The following object is masked from 'package:car':
recode
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
> library(geepack)
> library(survey)
Loading required package: grid
Loading required package: Matrix
Attaching package: 'survey'
The following object is masked from 'package:graphics':
dotchart
> library(testthat)
Attaching package: 'testthat'
The following object is masked from 'package:dplyr':
matches
>
> set.seed(123)
> test_check("Zelig")
Loading required package: Zelig
-- Imputation 1 --
1 2 3
-- Imputation 2 --
1 2 3
-- Imputation 3 --
1 2 3
-- Imputation 4 --
1 2 3
-- Imputation 5 --
1 2 3
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
-- Imputation 1 --
1 2
-- Imputation 2 --
1 2 3
-- Imputation 3 --
1 2
-- Imputation 4 --
1 2 3
-- Imputation 5 --
1 2
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Ben Goodrich, and Ying Lu. 2013.
normal-bayes: Bayesian Normal Linear Regression
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Andrew D. Martin, Kevin M. Quinn, and Jong Hee Park. 2013.
factor-bayes: Bayesian Factor Analysis
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Terry M. Therneau, and Thomas Lumley. 2011.
exp: Exponential Regression for Duration Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
gamma: Gamma Regression for Continuous, Positive Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
Calculating MLEs and large sample var-cov matrix.
This may take a moment...
Inverting Hessian to get large sample var-cov matrix.
Calculating MLEs and large sample var-cov matrix.
This may take a moment...
Inverting Hessian to get large sample var-cov matrix.
How to cite this model in Zelig:
Christian Kleiber and Achim Zeileis. 2008.
ivreg: Instrumental-Variable Regression
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Christian Kleiber and Achim Zeileis. 2008.
ivreg: Instrumental-Variable Regression
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
Model:
Call:
stats::glm(formula = vote ~ age + race, family = binomial("logit"),
data = as.data.frame(.))
Deviance Residuals:
Min 1Q Median 3Q Max
-1.9268 -1.2962 0.7072 0.7766 1.0723
Coefficients:
Estimate (OR) Std. Error (OR) z value Pr(>|z|)
(Intercept) 1.039111 0.183840 0.217 0.828325
age 1.011327 0.003088 3.689 0.000225 ***
racewhite 1.907038 0.256462 4.800 1.58e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 2266.7 on 1999 degrees of freedom
Residual deviance: 2228.8 on 1997 degrees of freedom
AIC: 2234.8
Number of Fisher Scoring iterations: 4
MCMClogit iteration 1 of 11000
beta =
0.00210
0.98533
Metropolis acceptance rate for beta = 1.00000
MCMClogit iteration 1101 of 11000
beta =
-0.01824
0.93814
Metropolis acceptance rate for beta = 0.52952
MCMClogit iteration 2201 of 11000
beta =
0.00475
0.95312
Metropolis acceptance rate for beta = 0.52158
MCMClogit iteration 3301 of 11000
beta =
-0.01742
0.98950
Metropolis acceptance rate for beta = 0.52802
MCMClogit iteration 4401 of 11000
beta =
-0.03785
0.98980
Metropolis acceptance rate for beta = 0.52602
MCMClogit iteration 5501 of 11000
beta =
-0.04772
0.92064
Metropolis acceptance rate for beta = 0.52209
MCMClogit iteration 6601 of 11000
beta =
0.08646
1.00539
Metropolis acceptance rate for beta = 0.52174
MCMClogit iteration 7701 of 11000
beta =
0.07100
1.01120
Metropolis acceptance rate for beta = 0.52578
MCMClogit iteration 8801 of 11000
beta =
-0.00420
0.88082
Metropolis acceptance rate for beta = 0.52460
MCMClogit iteration 9901 of 11000
beta =
-0.01473
0.87193
Metropolis acceptance rate for beta = 0.52227
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
The Metropolis acceptance rate for beta was 0.52218
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Patrick Lam. 2011.
normal-gee: General Estimating Equation for Normal Regression
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Patrick Lam. 2011.
normal-gee: General Estimating Equation for Normal Regression
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
Model: Combined Imputations
Estimate Std.Error z value Pr(>|z|)
(Intercept) 34.66099 2.83523 12.23 <2e-16
cyl -1.58728 0.49875 -3.18 0.0015
disp -0.02058 0.00696 -2.96 0.0031
For results from individual imputed datasets, use summary(x, subset = i:j)
Next step: Use 'setx' method
MCMCregress iteration 1 of 11000
beta =
0.01119
1.03455
sigma2 = 1.00941
MCMCregress iteration 1101 of 11000
beta =
-0.02594
1.06744
sigma2 = 1.03889
MCMCregress iteration 2201 of 11000
beta =
-0.05222
0.97402
sigma2 = 1.01563
MCMCregress iteration 3301 of 11000
beta =
-0.05933
0.97920
sigma2 = 1.00108
MCMCregress iteration 4401 of 11000
beta =
-0.01337
1.01322
sigma2 = 0.95198
MCMCregress iteration 5501 of 11000
beta =
-0.02224
1.04994
sigma2 = 0.99842
MCMCregress iteration 6601 of 11000
beta =
-0.02043
1.00096
sigma2 = 0.93645
MCMCregress iteration 7701 of 11000
beta =
0.01190
1.06590
sigma2 = 1.01251
MCMCregress iteration 8801 of 11000
beta =
-0.01996
1.01412
sigma2 = 0.95430
MCMCregress iteration 9901 of 11000
beta =
-0.03583
0.97622
sigma2 = 0.99748
How to cite this model in Zelig:
R Core Team. 2007.
poisson: Poisson Regression for Event Count Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
poisson: Poisson Regression for Event Count Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
MCMCpoisson iteration 1 of 11000
beta =
-0.01862
1.02535
Metropolis acceptance rate for beta = 1.00000
MCMCpoisson iteration 1101 of 11000
beta =
-0.00207
1.01122
Metropolis acceptance rate for beta = 0.53224
MCMCpoisson iteration 2201 of 11000
beta =
-0.02369
1.02072
Metropolis acceptance rate for beta = 0.51613
MCMCpoisson iteration 3301 of 11000
beta =
-0.01912
1.02585
Metropolis acceptance rate for beta = 0.51712
MCMCpoisson iteration 4401 of 11000
beta =
-0.05394
1.03940
Metropolis acceptance rate for beta = 0.51602
MCMCpoisson iteration 5501 of 11000
beta =
-0.04054
1.02780
Metropolis acceptance rate for beta = 0.51736
MCMCpoisson iteration 6601 of 11000
beta =
0.08526
0.96386
Metropolis acceptance rate for beta = 0.51750
MCMCpoisson iteration 7701 of 11000
beta =
0.03033
0.99266
Metropolis acceptance rate for beta = 0.52305
MCMCpoisson iteration 8801 of 11000
beta =
-0.00782
1.01038
Metropolis acceptance rate for beta = 0.52324
MCMCpoisson iteration 9901 of 11000
beta =
-0.00920
1.01052
Metropolis acceptance rate for beta = 0.52096
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
The Metropolis acceptance rate for beta was 0.51927
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
How to cite this model in Zelig:
R Core Team. 2007.
probit: Probit Regression for Dichotomous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
MCMCprobit iteration 1 of 11000
beta =
-0.01062
0.93045
MCMCprobit iteration 1101 of 11000
beta =
0.00854
0.98889
MCMCprobit iteration 2201 of 11000
beta =
-0.05915
1.05668
MCMCprobit iteration 3301 of 11000
beta =
-0.01819
0.86660
MCMCprobit iteration 4401 of 11000
beta =
-0.01958
0.95408
MCMCprobit iteration 5501 of 11000
beta =
-0.04281
0.93104
MCMCprobit iteration 6601 of 11000
beta =
-0.04593
0.95252
MCMCprobit iteration 7701 of 11000
beta =
0.01012
1.01175
MCMCprobit iteration 8801 of 11000
beta =
0.01985
1.03090
MCMCprobit iteration 9901 of 11000
beta =
-0.02902
0.99117
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
-- Imputation 1 --
1 2
-- Imputation 2 --
1 2
-- Imputation 3 --
1 2 3
-- Imputation 4 --
1 2 3
-- Imputation 5 --
1 2 3
How to cite this model in Zelig:
Alexander D'Amour. 2008.
quantile: Quantile Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau. 2022.
relogit: Rare Events Logistic Regression for Dichotomous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau. 2022.
relogit: Rare Events Logistic Regression for Dichotomous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau. 2022.
relogit: Rare Events Logistic Regression for Dichotomous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau. 2022.
relogit: Rare Events Logistic Regression for Dichotomous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
Model:
Call:
relogit(formula = cbind(conflict, 1 - conflict) ~ major + contig +
power + maxdem + mindem + years, data = as.data.frame(.),
tau = 0.00343020423212146, bias.correct = TRUE, case.control = "weighting")
Deviance Residuals:
Min 1Q Median 3Q Max
-0.94933 -0.04958 -0.02173 0.19019 0.48253
Coefficients:
Estimate Std. Error (robust) z value Pr(>|z|)
(Intercept) -6.61889 0.31748 -20.848 < 2e-16 ***
major 1.67218 0.27842 6.006 1.9e-09 ***
contig 4.01640 0.22954 17.498 < 2e-16 ***
power 0.28836 0.41574 0.694 0.487925
maxdem 0.06629 0.01925 3.444 0.000573 ***
mindem -0.08143 0.02996 -2.718 0.006572 **
years -0.11707 0.01336 -8.764 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 143.116 on 3125 degrees of freedom
Residual deviance: 91.178 on 3119 degrees of freedom
AIC: 27.041
Number of Fisher Scoring iterations: 10
Model:
Call:
relogit(formula = cbind(conflict, 1 - conflict) ~ major + contig +
power + maxdem + mindem + years, data = as.data.frame(.),
tau = 0.00343020423212146, bias.correct = TRUE, case.control = "weighting")
Deviance Residuals:
Min 1Q Median 3Q Max
-0.94933 -0.04958 -0.02173 0.19019 0.48253
Coefficients:
Estimate Std. Error (robust) z value Pr(>|z|)
(Intercept) -6.61889 0.31748 -20.848 < 2e-16 ***
major 1.67218 0.27842 6.006 1.9e-09 ***
contig 4.01640 0.22954 17.498 < 2e-16 ***
power 0.28836 0.41574 0.694 0.487925
maxdem 0.06629 0.01925 3.444 0.000573 ***
mindem -0.08143 0.02996 -2.718 0.006572 **
years -0.11707 0.01336 -8.764 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 143.116 on 3125 degrees of freedom
Residual deviance: 91.178 on 3119 degrees of freedom
AIC: 27.041
Number of Fisher Scoring iterations: 10
Model:
Call:
relogit(formula = cbind(conflict, 1 - conflict) ~ major + contig +
power + maxdem + mindem + years, data = as.data.frame(.),
tau = 0.00343020423212146, bias.correct = TRUE, case.control = "weighting")
Deviance Residuals:
Min 1Q Median 3Q Max
-0.94933 -0.04958 -0.02173 0.19019 0.48253
Coefficients:
Estimate (OR) Std. Error (OR, robust) z value Pr(>|z|)
(Intercept) 0.001335 0.001521 -20.848 < 2e-16 ***
major 5.323748 4.119262 6.006 1.9e-09 ***
contig 55.501077 41.045844 17.498 < 2e-16 ***
power 1.334239 1.410153 0.694 0.487925
maxdem 1.068533 0.054366 3.444 0.000573 ***
mindem 0.921799 0.067683 -2.718 0.006572 **
years 0.889520 0.041892 -8.764 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 143.116 on 3125 degrees of freedom
Residual deviance: 91.178 on 3119 degrees of freedom
AIC: 27.041
Number of Fisher Scoring iterations: 10
Model:
Call:
z5$zelig(formula = conflict ~ major + contig + power + maxdem +
mindem + years, tau = 1042/303772, case.control = "prior",
data = mid)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.0596 -0.0376 -0.0231 2.1085 4.4649
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -7.525688 0.179685 -41.883 < 2e-16
major 2.433432 0.157561 15.444 < 2e-16
contig 4.112491 0.157650 26.086 < 2e-16
power 1.053747 0.217243 4.851 1.23e-06
maxdem 0.048431 0.010065 4.812 1.50e-06
mindem -0.065249 0.012802 -5.097 3.45e-07
years -0.063359 0.005705 -11.106 < 2e-16
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 3979.5 on 3125 degrees of freedom
Residual deviance: 1868.5 on 3119 degrees of freedom
AIC: 1882.5
Number of Fisher Scoring iterations: 6
Next step: Use 'setx' method
Model:
Call:
`z5$zelig`(formula = conflict ~ major + contig + power + maxdem +
mindem + years, tau = 1042/303772, case.control = "prior",
data = mid)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.0596 -0.0376 -0.0231 2.1085 4.4649
Coefficients:
Estimate (OR) Std. Error (OR) z value Pr(>|z|)
(Intercept) 5.391e-04 9.686e-05 -41.883 < 2e-16 ***
major 1.140e+01 1.796e+00 15.444 < 2e-16 ***
contig 6.110e+01 9.632e+00 26.086 < 2e-16 ***
power 2.868e+00 6.231e-01 4.851 1.23e-06 ***
maxdem 1.050e+00 1.056e-02 4.812 1.50e-06 ***
mindem 9.368e-01 1.199e-02 -5.097 3.45e-07 ***
years 9.386e-01 5.355e-03 -11.106 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 3979.5 on 3125 degrees of freedom
Residual deviance: 1868.5 on 3119 degrees of freedom
AIC: 1882.5
Number of Fisher Scoring iterations: 6
How to cite this model in Zelig:
Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau. 2022.
relogit: Rare Events Logistic Regression for Dichotomous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
MCMCtobit iteration 1 of 11000
beta =
0.09918
0.93061
sigma2 = 0.70553
MCMCtobit iteration 1101 of 11000
beta =
0.07445
0.91668
sigma2 = 0.99194
MCMCtobit iteration 2201 of 11000
beta =
0.00068
0.99780
sigma2 = 0.99760
MCMCtobit iteration 3301 of 11000
beta =
0.04504
0.95894
sigma2 = 0.95844
MCMCtobit iteration 4401 of 11000
beta =
-0.03116
1.00165
sigma2 = 0.97216
MCMCtobit iteration 5501 of 11000
beta =
-0.01771
0.98161
sigma2 = 0.94482
MCMCtobit iteration 6601 of 11000
beta =
-0.00829
0.96500
sigma2 = 0.90195
MCMCtobit iteration 7701 of 11000
beta =
0.09984
0.93619
sigma2 = 1.03124
MCMCtobit iteration 8801 of 11000
beta =
0.03092
0.99268
sigma2 = 0.95955
MCMCtobit iteration 9901 of 11000
beta =
0.04477
0.93956
sigma2 = 1.01930
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
Noninteger weights were set, but the model in Zelig is only able to use integer valued weights.
A bootstrapped version of the dataset was constructed using the weights as sample probabilities.
How to cite this model in Zelig:
R Core Team. 2007.
logit: Logistic Regression for Dichotomous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
logit: Logistic Regression for Dichotomous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
poisson: Poisson Regression for Event Count Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
poisson: Poisson Regression for Event Count Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
setx:
(Intercept) x
1 1 0
setx1:
(Intercept) x
1 1 1
Next step: Use 'sim' method
sim x :
-----
ev
mean sd 50% 2.5% 97.5%
1 -0.000499563 0.006496101 -0.0005420348 -0.01340449 0.01259395
pv
mean sd 50% 2.5% 97.5%
[1,] -0.002512827 0.09819415 -0.003183565 -0.2043797 0.1944359
sim x1 :
-----
ev
mean sd 50% 2.5% 97.5%
1 1.00022 0.006045548 1.000062 0.9887536 1.01215
pv
mean sd 50% 2.5% 97.5%
[1,] 0.9993027 0.09953018 0.9999023 0.8025547 1.197061
fd
mean sd 50% 2.5% 97.5%
1 1.000719 0.01076163 1.000655 0.9799646 1.022849
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
Model: Combined Bootstraps
Estimate Std.Error z value Pr(>|z|)
(Intercept) -0.0003 0.0060 -0.05 0.96
x 1.0006 0.0115 86.67 <2e-16
For results from individual bootstrapped datasets, use summary(x, subset = i:j)
Next step: Use 'setx' method
Bootstrapped Dataset 2
Call:
z5$zelig(formula = formula, data = data, by = by, bootstrap = 20)
Residuals:
Min 1Q Median 3Q Max
-0.09973 -0.09750 -0.09523 0.10254 0.10486
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.004872 0.006185 -0.788 0.431
x 1.004608 0.010780 93.194 <2e-16
Residual standard error: 0.1001 on 998 degrees of freedom
Multiple R-squared: 0.8969, Adjusted R-squared: 0.8968
F-statistic: 8685 on 1 and 998 DF, p-value: < 2.2e-16
Bootstrapped Dataset 3
Call:
z5$zelig(formula = formula, data = data, by = by, bootstrap = 20)
Residuals:
Min 1Q Median 3Q Max
-0.10360 -0.09514 -0.08812 0.10411 0.11242
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.003619 0.006352 0.57 0.569
x 0.983959 0.010756 91.48 <2e-16
Residual standard error: 0.09988 on 998 degrees of freedom
Multiple R-squared: 0.8934, Adjusted R-squared: 0.8933
F-statistic: 8368 on 1 and 998 DF, p-value: < 2.2e-16
Next step: Use 'setx' method
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
Model: Combined Imputations
Estimate Std.Error z value Pr(>|z|)
(Intercept) -1.001 1.733 -0.58 0.56
x 1.001 0.011 91.25 <2e-16
For results from individual imputed datasets, use summary(x, subset = i:j)
Next step: Use 'setx' method
Imputed Dataset 1
Call:
z5$zelig(formula = formula, data = data, by = by)
Residuals:
Min 1Q Median 3Q Max
-0.1003 -0.1000 0.0000 0.1000 0.1003
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.0003003 0.0063356 -0.047 0.962
x 1.0006000 0.0109654 91.251 <2e-16
Residual standard error: 0.1001 on 998 degrees of freedom
Multiple R-squared: 0.893, Adjusted R-squared: 0.8929
F-statistic: 8327 on 1 and 998 DF, p-value: < 2.2e-16
Next step: Use 'setx' method
Model:
$rr
[1] 0
Call:
zb.out$zelig(formula = yb ~ xx + zz, data = data, by = "rr")
Deviance Residuals:
Min 1Q Median 3Q Max
-2.6031 0.2531 0.3125 0.3947 0.6564
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.274 1.045 1.219 0.223
xx 1.088 1.195 0.910 0.363
zz 1.975 2.152 0.918 0.359
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 28.820 on 55 degrees of freedom
Residual deviance: 26.896 on 53 degrees of freedom
AIC: 32.896
Number of Fisher Scoring iterations: 6
Model:
$rr
[1] 1
Call:
zb.out$zelig(formula = yb ~ xx + zz, data = data, by = "rr")
Deviance Residuals:
Min 1Q Median 3Q Max
-2.6982 0.2266 0.3859 0.7298 1.0780
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.2255 1.0561 -0.214 0.831
xx 2.2702 1.1632 1.952 0.051
zz 2.1285 1.8878 1.128 0.260
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 41.724 on 43 degrees of freedom
Residual deviance: 35.553 on 41 degrees of freedom
AIC: 41.553
Number of Fisher Scoring iterations: 5
Next step: Use 'setx' method
sim x :
-----
ev
mean sd 50% 2.5% 97.5%
[1,] 0.9476733 0.05386777 0.9665833 0.8009766 0.9955033
pv
0 1
[1,] 0.058 0.942
sim x :
-----
ev
mean sd 50% 2.5% 97.5%
[1,] 0.9367684 0.07021485 0.9611249 0.7428204 0.9949634
pv
0 1
[1,] 0.061 0.939
sim x1 :
-----
ev
mean sd 50% 2.5% 97.5%
[1,] 0.8918403 0.06422275 0.9049403 0.7298034 0.9740786
pv
0 1
[1,] 0.101 0.899
fd
mean sd 50% 2.5% 97.5%
[1,] -0.05583303 0.07977401 -0.05088306 -0.2221262 0.1014462
sim x1 :
-----
ev
mean sd 50% 2.5% 97.5%
[1,] 0.6990619 0.09467077 0.7081549 0.5028567 0.8603348
pv
0 1
[1,] 0.31 0.69
fd
mean sd 50% 2.5% 97.5%
[1,] -0.2377065 0.1148547 -0.2380952 -0.466211 -0.01659926
[ FAIL 1 | WARN 13 | SKIP 17 | PASS 212 ]
══ Skipped tests ═══════════════════════════════════════════════════════════════
• On CRAN (17)
══ Failed tests ════════════════════════════════════════════════════════════════
── Error (test-interface.R:74:5): REQUIRE TEST zelig_qi_to_df multinomial outcome ──
Error in `if (mcmc.method == "RWM") {
auto.Scythe.call(output.object = "posterior", cc.fun.name = "MCMCmnlMH",
sample.nonconst = sample, Y = Y, X = X, burnin = as.integer(burnin),
mcmc = as.integer(mcmc), thin = as.integer(thin), tune = tune,
lecuyer = as.integer(lecuyer), seedarray = as.integer(seed.array),
lecuyerstream = as.integer(lecuyer.stream), verbose = as.integer(verbose),
betastart = beta.start, betamode = beta.mode, b0 = b0,
B0 = B0, V = V, RW = as.integer(1), tdf = as.double(tdf))
output <- form.mcmc.object(posterior, names = xnames, title = "MCMCmnl Posterior Sample")
} else if (mcmc.method == "IndMH") {
auto.Scythe.call(output.object = "posterior", cc.fun.name = "MCMCmnlMH",
sample.nonconst = sample, Y = Y, X = X, burnin = as.integer(burnin),
mcmc = as.integer(mcmc), thin = as.integer(thin), tune = tune,
lecuyer = as.integer(lecuyer), seedarray = as.integer(seed.array),
lecuyerstream = as.integer(lecuyer.stream), verbose = as.integer(verbose),
betastart = beta.start, betamode = beta.mode, b0 = b0,
B0 = B0, V = V, RW = as.integer(0), tdf = as.double(tdf))
output <- form.mcmc.object(posterior, names = xnames, title = "MCMCmnl Posterior Sample")
} else if (mcmc.method == "slice") {
auto.Scythe.call(output.object = "posterior", cc.fun.name = "MCMCmnlslice",
sample.nonconst = sample, Y = Y, X = X, burnin = as.integer(burnin),
mcmc = as.integer(mcmc), thin = as.integer(thin), lecuyer = as.integer(lecuyer),
seedarray = as.integer(seed.array), lecuyerstream = as.integer(lecuyer.stream),
verbose = as.integer(verbose), betastart = beta.start,
b0 = b0, B0 = B0, V = V)
output <- form.mcmc.object(posterior, names = xnames, title = "MCMCmnl Posterior Sample")
} else {
cat("\n\nmcmc.method not equal to one of 'RWM', 'IndMH', or 'slice'.\n")
stop("Please respecifify and call MCMCmnl() again.\n")
}`: the condition has length > 1
Backtrace:
▆
1. ├─... %>% zelig_qi_to_df() at test-interface.R:74:4
2. ├─Zelig::zelig_qi_to_df(.)
3. │ └─Zelig::is_zelig(obj)
4. ├─Zelig::sim(.)
5. │ └─Zelig::is_zelig(obj)
6. ├─Zelig::setx(.)
7. │ └─Zelig::is_zelig(obj, fail = FALSE)
8. ├─Zelig::zelig(...)
9. │ └─z5$zelig(formula = formula, data = data, ..., by = by)
10. │ └─Zelig callSuper(formula = formula, data = data, ..., by = by, bootstrap = FALSE)
11. │ └─.self$data %>% group_by_(.self$by) %>% ...
12. ├─dplyr::do(., z.out = eval(fn2(.self$model.call, quote(as.data.frame(.)))))
13. ├─dplyr:::do.grouped_df(., z.out = eval(fn2(.self$model.call, quote(as.data.frame(.)))))
14. │ └─rlang::eval_tidy(args[[j]], mask)
15. └─base::eval(fn2(.self$model.call, quote(as.data.frame(.))))
16. └─base::eval(fn2(.self$model.call, quote(as.data.frame(.))))
17. └─MCMCpack::MCMCmnl(...)
[ FAIL 1 | WARN 13 | SKIP 17 | PASS 212 ]
Error: Test failures
Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang
Version: 5.1.7
Check: tests
Result: ERROR
Running ‘testthat.R’ [333s/467s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(AER)
Loading required package: car
Loading required package: carData
Loading required package: lmtest
Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, as.Date.numeric
Loading required package: sandwich
Loading required package: survival
> library(dplyr)
Attaching package: 'dplyr'
The following object is masked from 'package:car':
recode
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
> library(geepack)
> library(survey)
Loading required package: grid
Loading required package: Matrix
Attaching package: 'survey'
The following object is masked from 'package:graphics':
dotchart
> library(testthat)
Attaching package: 'testthat'
The following object is masked from 'package:dplyr':
matches
>
> set.seed(123)
> test_check("Zelig")
Loading required package: Zelig
-- Imputation 1 --
1 2 3
-- Imputation 2 --
1 2 3
-- Imputation 3 --
1 2 3
-- Imputation 4 --
1 2 3
-- Imputation 5 --
1 2 3
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
-- Imputation 1 --
1 2
-- Imputation 2 --
1 2 3
-- Imputation 3 --
1 2
-- Imputation 4 --
1 2 3
-- Imputation 5 --
1 2
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Ben Goodrich, and Ying Lu. 2013.
normal-bayes: Bayesian Normal Linear Regression
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Andrew D. Martin, Kevin M. Quinn, and Jong Hee Park. 2013.
factor-bayes: Bayesian Factor Analysis
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Terry M. Therneau, and Thomas Lumley. 2011.
exp: Exponential Regression for Duration Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
gamma: Gamma Regression for Continuous, Positive Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
Calculating MLEs and large sample var-cov matrix.
This may take a moment...
Inverting Hessian to get large sample var-cov matrix.
Calculating MLEs and large sample var-cov matrix.
This may take a moment...
Inverting Hessian to get large sample var-cov matrix.
How to cite this model in Zelig:
Christian Kleiber and Achim Zeileis. 2008.
ivreg: Instrumental-Variable Regression
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Christian Kleiber and Achim Zeileis. 2008.
ivreg: Instrumental-Variable Regression
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
Model:
Call:
stats::glm(formula = vote ~ age + race, family = binomial("logit"),
data = as.data.frame(.))
Deviance Residuals:
Min 1Q Median 3Q Max
-1.9268 -1.2962 0.7072 0.7766 1.0723
Coefficients:
Estimate (OR) Std. Error (OR) z value Pr(>|z|)
(Intercept) 1.039111 0.183840 0.217 0.828325
age 1.011327 0.003088 3.689 0.000225 ***
racewhite 1.907038 0.256462 4.800 1.58e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 2266.7 on 1999 degrees of freedom
Residual deviance: 2228.8 on 1997 degrees of freedom
AIC: 2234.8
Number of Fisher Scoring iterations: 4
MCMClogit iteration 1 of 11000
beta =
0.00210
0.98533
Metropolis acceptance rate for beta = 1.00000
MCMClogit iteration 1101 of 11000
beta =
-0.01824
0.93814
Metropolis acceptance rate for beta = 0.52952
MCMClogit iteration 2201 of 11000
beta =
0.00475
0.95312
Metropolis acceptance rate for beta = 0.52158
MCMClogit iteration 3301 of 11000
beta =
-0.01742
0.98950
Metropolis acceptance rate for beta = 0.52802
MCMClogit iteration 4401 of 11000
beta =
-0.03785
0.98980
Metropolis acceptance rate for beta = 0.52602
MCMClogit iteration 5501 of 11000
beta =
-0.04772
0.92064
Metropolis acceptance rate for beta = 0.52209
MCMClogit iteration 6601 of 11000
beta =
0.08646
1.00539
Metropolis acceptance rate for beta = 0.52174
MCMClogit iteration 7701 of 11000
beta =
0.07100
1.01120
Metropolis acceptance rate for beta = 0.52578
MCMClogit iteration 8801 of 11000
beta =
-0.00420
0.88082
Metropolis acceptance rate for beta = 0.52460
MCMClogit iteration 9901 of 11000
beta =
-0.01473
0.87193
Metropolis acceptance rate for beta = 0.52227
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
The Metropolis acceptance rate for beta was 0.52218
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Patrick Lam. 2011.
normal-gee: General Estimating Equation for Normal Regression
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Patrick Lam. 2011.
normal-gee: General Estimating Equation for Normal Regression
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
Model: Combined Imputations
Estimate Std.Error z value Pr(>|z|)
(Intercept) 34.66099 2.83523 12.23 <2e-16
cyl -1.58728 0.49875 -3.18 0.0015
disp -0.02058 0.00696 -2.96 0.0031
For results from individual imputed datasets, use summary(x, subset = i:j)
Next step: Use 'setx' method
MCMCregress iteration 1 of 11000
beta =
0.01119
1.03455
sigma2 = 1.00941
MCMCregress iteration 1101 of 11000
beta =
-0.02594
1.06744
sigma2 = 1.03889
MCMCregress iteration 2201 of 11000
beta =
-0.05222
0.97402
sigma2 = 1.01563
MCMCregress iteration 3301 of 11000
beta =
-0.05933
0.97920
sigma2 = 1.00108
MCMCregress iteration 4401 of 11000
beta =
-0.01337
1.01322
sigma2 = 0.95198
MCMCregress iteration 5501 of 11000
beta =
-0.02224
1.04994
sigma2 = 0.99842
MCMCregress iteration 6601 of 11000
beta =
-0.02043
1.00096
sigma2 = 0.93645
MCMCregress iteration 7701 of 11000
beta =
0.01190
1.06590
sigma2 = 1.01251
MCMCregress iteration 8801 of 11000
beta =
-0.01996
1.01412
sigma2 = 0.95430
MCMCregress iteration 9901 of 11000
beta =
-0.03583
0.97622
sigma2 = 0.99748
How to cite this model in Zelig:
R Core Team. 2007.
poisson: Poisson Regression for Event Count Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
poisson: Poisson Regression for Event Count Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
MCMCpoisson iteration 1 of 11000
beta =
-0.01862
1.02535
Metropolis acceptance rate for beta = 1.00000
MCMCpoisson iteration 1101 of 11000
beta =
-0.00207
1.01122
Metropolis acceptance rate for beta = 0.53224
MCMCpoisson iteration 2201 of 11000
beta =
-0.02369
1.02072
Metropolis acceptance rate for beta = 0.51613
MCMCpoisson iteration 3301 of 11000
beta =
-0.01912
1.02585
Metropolis acceptance rate for beta = 0.51712
MCMCpoisson iteration 4401 of 11000
beta =
-0.05394
1.03940
Metropolis acceptance rate for beta = 0.51602
MCMCpoisson iteration 5501 of 11000
beta =
-0.04054
1.02780
Metropolis acceptance rate for beta = 0.51736
MCMCpoisson iteration 6601 of 11000
beta =
0.08526
0.96386
Metropolis acceptance rate for beta = 0.51750
MCMCpoisson iteration 7701 of 11000
beta =
0.03033
0.99266
Metropolis acceptance rate for beta = 0.52305
MCMCpoisson iteration 8801 of 11000
beta =
-0.00782
1.01038
Metropolis acceptance rate for beta = 0.52324
MCMCpoisson iteration 9901 of 11000
beta =
-0.00920
1.01052
Metropolis acceptance rate for beta = 0.52096
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
The Metropolis acceptance rate for beta was 0.51927
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
How to cite this model in Zelig:
R Core Team. 2007.
probit: Probit Regression for Dichotomous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
MCMCprobit iteration 1 of 11000
beta =
-0.01062
0.93045
MCMCprobit iteration 1101 of 11000
beta =
0.00854
0.98889
MCMCprobit iteration 2201 of 11000
beta =
-0.05915
1.05668
MCMCprobit iteration 3301 of 11000
beta =
-0.01819
0.86660
MCMCprobit iteration 4401 of 11000
beta =
-0.01958
0.95408
MCMCprobit iteration 5501 of 11000
beta =
-0.04281
0.93104
MCMCprobit iteration 6601 of 11000
beta =
-0.04593
0.95252
MCMCprobit iteration 7701 of 11000
beta =
0.01012
1.01175
MCMCprobit iteration 8801 of 11000
beta =
0.01985
1.03090
MCMCprobit iteration 9901 of 11000
beta =
-0.02902
0.99117
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
-- Imputation 1 --
1 2
-- Imputation 2 --
1 2
-- Imputation 3 --
1 2 3
-- Imputation 4 --
1 2 3
-- Imputation 5 --
1 2 3
How to cite this model in Zelig:
Alexander D'Amour. 2008.
quantile: Quantile Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau. 2022.
relogit: Rare Events Logistic Regression for Dichotomous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau. 2022.
relogit: Rare Events Logistic Regression for Dichotomous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau. 2022.
relogit: Rare Events Logistic Regression for Dichotomous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau. 2022.
relogit: Rare Events Logistic Regression for Dichotomous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
Model:
Call:
relogit(formula = cbind(conflict, 1 - conflict) ~ major + contig +
power + maxdem + mindem + years, data = as.data.frame(.),
tau = 0.00343020423212146, bias.correct = TRUE, case.control = "weighting")
Deviance Residuals:
Min 1Q Median 3Q Max
-0.94933 -0.04958 -0.02173 0.19019 0.48253
Coefficients:
Estimate Std. Error (robust) z value Pr(>|z|)
(Intercept) -6.61889 0.31748 -20.848 < 2e-16 ***
major 1.67218 0.27842 6.006 1.9e-09 ***
contig 4.01640 0.22954 17.498 < 2e-16 ***
power 0.28836 0.41574 0.694 0.487925
maxdem 0.06629 0.01925 3.444 0.000573 ***
mindem -0.08143 0.02996 -2.718 0.006572 **
years -0.11707 0.01336 -8.764 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 143.116 on 3125 degrees of freedom
Residual deviance: 91.178 on 3119 degrees of freedom
AIC: 27.041
Number of Fisher Scoring iterations: 10
Model:
Call:
relogit(formula = cbind(conflict, 1 - conflict) ~ major + contig +
power + maxdem + mindem + years, data = as.data.frame(.),
tau = 0.00343020423212146, bias.correct = TRUE, case.control = "weighting")
Deviance Residuals:
Min 1Q Median 3Q Max
-0.94933 -0.04958 -0.02173 0.19019 0.48253
Coefficients:
Estimate Std. Error (robust) z value Pr(>|z|)
(Intercept) -6.61889 0.31748 -20.848 < 2e-16 ***
major 1.67218 0.27842 6.006 1.9e-09 ***
contig 4.01640 0.22954 17.498 < 2e-16 ***
power 0.28836 0.41574 0.694 0.487925
maxdem 0.06629 0.01925 3.444 0.000573 ***
mindem -0.08143 0.02996 -2.718 0.006572 **
years -0.11707 0.01336 -8.764 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 143.116 on 3125 degrees of freedom
Residual deviance: 91.178 on 3119 degrees of freedom
AIC: 27.041
Number of Fisher Scoring iterations: 10
Model:
Call:
relogit(formula = cbind(conflict, 1 - conflict) ~ major + contig +
power + maxdem + mindem + years, data = as.data.frame(.),
tau = 0.00343020423212146, bias.correct = TRUE, case.control = "weighting")
Deviance Residuals:
Min 1Q Median 3Q Max
-0.94933 -0.04958 -0.02173 0.19019 0.48253
Coefficients:
Estimate (OR) Std. Error (OR, robust) z value Pr(>|z|)
(Intercept) 0.001335 0.001521 -20.848 < 2e-16 ***
major 5.323748 4.119262 6.006 1.9e-09 ***
contig 55.501077 41.045844 17.498 < 2e-16 ***
power 1.334239 1.410153 0.694 0.487925
maxdem 1.068533 0.054366 3.444 0.000573 ***
mindem 0.921799 0.067683 -2.718 0.006572 **
years 0.889520 0.041892 -8.764 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 143.116 on 3125 degrees of freedom
Residual deviance: 91.178 on 3119 degrees of freedom
AIC: 27.041
Number of Fisher Scoring iterations: 10
Model:
Call:
z5$zelig(formula = conflict ~ major + contig + power + maxdem +
mindem + years, tau = 1042/303772, case.control = "prior",
data = mid)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.0596 -0.0376 -0.0231 2.1085 4.4649
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -7.525688 0.179685 -41.883 < 2e-16
major 2.433432 0.157561 15.444 < 2e-16
contig 4.112491 0.157650 26.086 < 2e-16
power 1.053747 0.217243 4.851 1.23e-06
maxdem 0.048431 0.010065 4.812 1.50e-06
mindem -0.065249 0.012802 -5.097 3.45e-07
years -0.063359 0.005705 -11.106 < 2e-16
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 3979.5 on 3125 degrees of freedom
Residual deviance: 1868.5 on 3119 degrees of freedom
AIC: 1882.5
Number of Fisher Scoring iterations: 6
Next step: Use 'setx' method
Model:
Call:
`z5$zelig`(formula = conflict ~ major + contig + power + maxdem +
mindem + years, tau = 1042/303772, case.control = "prior",
data = mid)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.0596 -0.0376 -0.0231 2.1085 4.4649
Coefficients:
Estimate (OR) Std. Error (OR) z value Pr(>|z|)
(Intercept) 5.391e-04 9.686e-05 -41.883 < 2e-16 ***
major 1.140e+01 1.796e+00 15.444 < 2e-16 ***
contig 6.110e+01 9.632e+00 26.086 < 2e-16 ***
power 2.868e+00 6.231e-01 4.851 1.23e-06 ***
maxdem 1.050e+00 1.056e-02 4.812 1.50e-06 ***
mindem 9.368e-01 1.199e-02 -5.097 3.45e-07 ***
years 9.386e-01 5.355e-03 -11.106 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 3979.5 on 3125 degrees of freedom
Residual deviance: 1868.5 on 3119 degrees of freedom
AIC: 1882.5
Number of Fisher Scoring iterations: 6
How to cite this model in Zelig:
Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau. 2022.
relogit: Rare Events Logistic Regression for Dichotomous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
MCMCtobit iteration 1 of 11000
beta =
0.09918
0.93061
sigma2 = 0.70553
MCMCtobit iteration 1101 of 11000
beta =
0.07445
0.91668
sigma2 = 0.99194
MCMCtobit iteration 2201 of 11000
beta =
0.00068
0.99780
sigma2 = 0.99760
MCMCtobit iteration 3301 of 11000
beta =
0.04504
0.95894
sigma2 = 0.95844
MCMCtobit iteration 4401 of 11000
beta =
-0.03116
1.00165
sigma2 = 0.97216
MCMCtobit iteration 5501 of 11000
beta =
-0.01771
0.98161
sigma2 = 0.94482
MCMCtobit iteration 6601 of 11000
beta =
-0.00829
0.96500
sigma2 = 0.90195
MCMCtobit iteration 7701 of 11000
beta =
0.09984
0.93619
sigma2 = 1.03124
MCMCtobit iteration 8801 of 11000
beta =
0.03092
0.99268
sigma2 = 0.95955
MCMCtobit iteration 9901 of 11000
beta =
0.04477
0.93956
sigma2 = 1.01930
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
Noninteger weights were set, but the model in Zelig is only able to use integer valued weights.
A bootstrapped version of the dataset was constructed using the weights as sample probabilities.
How to cite this model in Zelig:
R Core Team. 2007.
logit: Logistic Regression for Dichotomous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
logit: Logistic Regression for Dichotomous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
poisson: Poisson Regression for Event Count Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
poisson: Poisson Regression for Event Count Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
setx:
(Intercept) x
1 1 0
setx1:
(Intercept) x
1 1 1
Next step: Use 'sim' method
sim x :
-----
ev
mean sd 50% 2.5% 97.5%
1 -0.000499563 0.006496101 -0.0005420348 -0.01340449 0.01259395
pv
mean sd 50% 2.5% 97.5%
[1,] -0.002512827 0.09819415 -0.003183565 -0.2043797 0.1944359
sim x1 :
-----
ev
mean sd 50% 2.5% 97.5%
1 1.00022 0.006045548 1.000062 0.9887536 1.01215
pv
mean sd 50% 2.5% 97.5%
[1,] 0.9993027 0.09953018 0.9999023 0.8025547 1.197061
fd
mean sd 50% 2.5% 97.5%
1 1.000719 0.01076163 1.000655 0.9799646 1.022849
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
Model: Combined Bootstraps
Estimate Std.Error z value Pr(>|z|)
(Intercept) -0.0003 0.0060 -0.05 0.96
x 1.0006 0.0115 86.67 <2e-16
For results from individual bootstrapped datasets, use summary(x, subset = i:j)
Next step: Use 'setx' method
Bootstrapped Dataset 2
Call:
z5$zelig(formula = formula, data = data, by = by, bootstrap = 20)
Residuals:
Min 1Q Median 3Q Max
-0.09973 -0.09750 -0.09523 0.10254 0.10486
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.004872 0.006185 -0.788 0.431
x 1.004608 0.010780 93.194 <2e-16
Residual standard error: 0.1001 on 998 degrees of freedom
Multiple R-squared: 0.8969, Adjusted R-squared: 0.8968
F-statistic: 8685 on 1 and 998 DF, p-value: < 2.2e-16
Bootstrapped Dataset 3
Call:
z5$zelig(formula = formula, data = data, by = by, bootstrap = 20)
Residuals:
Min 1Q Median 3Q Max
-0.10360 -0.09514 -0.08812 0.10411 0.11242
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.003619 0.006352 0.57 0.569
x 0.983959 0.010756 91.48 <2e-16
Residual standard error: 0.09988 on 998 degrees of freedom
Multiple R-squared: 0.8934, Adjusted R-squared: 0.8933
F-statistic: 8368 on 1 and 998 DF, p-value: < 2.2e-16
Next step: Use 'setx' method
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
Model: Combined Imputations
Estimate Std.Error z value Pr(>|z|)
(Intercept) -1.001 1.733 -0.58 0.56
x 1.001 0.011 91.25 <2e-16
For results from individual imputed datasets, use summary(x, subset = i:j)
Next step: Use 'setx' method
Imputed Dataset 1
Call:
z5$zelig(formula = formula, data = data, by = by)
Residuals:
Min 1Q Median 3Q Max
-0.1003 -0.1000 0.0000 0.1000 0.1003
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.0003003 0.0063356 -0.047 0.962
x 1.0006000 0.0109654 91.251 <2e-16
Residual standard error: 0.1001 on 998 degrees of freedom
Multiple R-squared: 0.893, Adjusted R-squared: 0.8929
F-statistic: 8327 on 1 and 998 DF, p-value: < 2.2e-16
Next step: Use 'setx' method
Model:
$rr
[1] 0
Call:
zb.out$zelig(formula = yb ~ xx + zz, data = data, by = "rr")
Deviance Residuals:
Min 1Q Median 3Q Max
-2.6031 0.2531 0.3125 0.3947 0.6564
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.274 1.045 1.219 0.223
xx 1.088 1.195 0.910 0.363
zz 1.975 2.152 0.918 0.359
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 28.820 on 55 degrees of freedom
Residual deviance: 26.896 on 53 degrees of freedom
AIC: 32.896
Number of Fisher Scoring iterations: 6
Model:
$rr
[1] 1
Call:
zb.out$zelig(formula = yb ~ xx + zz, data = data, by = "rr")
Deviance Residuals:
Min 1Q Median 3Q Max
-2.6982 0.2266 0.3859 0.7298 1.0780
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.2255 1.0561 -0.214 0.831
xx 2.2702 1.1632 1.952 0.051
zz 2.1285 1.8878 1.128 0.260
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 41.724 on 43 degrees of freedom
Residual deviance: 35.553 on 41 degrees of freedom
AIC: 41.553
Number of Fisher Scoring iterations: 5
Next step: Use 'setx' method
sim x :
-----
ev
mean sd 50% 2.5% 97.5%
[1,] 0.9476733 0.05386777 0.9665833 0.8009766 0.9955033
pv
0 1
[1,] 0.058 0.942
sim x :
-----
ev
mean sd 50% 2.5% 97.5%
[1,] 0.9367684 0.07021485 0.9611249 0.7428204 0.9949634
pv
0 1
[1,] 0.061 0.939
sim x1 :
-----
ev
mean sd 50% 2.5% 97.5%
[1,] 0.8918403 0.06422275 0.9049403 0.7298034 0.9740786
pv
0 1
[1,] 0.101 0.899
fd
mean sd 50% 2.5% 97.5%
[1,] -0.05583303 0.07977401 -0.05088306 -0.2221262 0.1014462
sim x1 :
-----
ev
mean sd 50% 2.5% 97.5%
[1,] 0.6990619 0.09467077 0.7081549 0.5028567 0.8603348
pv
0 1
[1,] 0.31 0.69
fd
mean sd 50% 2.5% 97.5%
[1,] -0.2377065 0.1148547 -0.2380952 -0.466211 -0.01659926
[ FAIL 1 | WARN 13 | SKIP 17 | PASS 212 ]
══ Skipped tests ═══════════════════════════════════════════════════════════════
• On CRAN (17)
══ Failed tests ════════════════════════════════════════════════════════════════
── Error (test-interface.R:74:5): REQUIRE TEST zelig_qi_to_df multinomial outcome ──
Error in `if (mcmc.method == "RWM") {
auto.Scythe.call(output.object = "posterior", cc.fun.name = "MCMCmnlMH",
sample.nonconst = sample, Y = Y, X = X, burnin = as.integer(burnin),
mcmc = as.integer(mcmc), thin = as.integer(thin), tune = tune,
lecuyer = as.integer(lecuyer), seedarray = as.integer(seed.array),
lecuyerstream = as.integer(lecuyer.stream), verbose = as.integer(verbose),
betastart = beta.start, betamode = beta.mode, b0 = b0,
B0 = B0, V = V, RW = as.integer(1), tdf = as.double(tdf))
output <- form.mcmc.object(posterior, names = xnames, title = "MCMCmnl Posterior Sample")
} else if (mcmc.method == "IndMH") {
auto.Scythe.call(output.object = "posterior", cc.fun.name = "MCMCmnlMH",
sample.nonconst = sample, Y = Y, X = X, burnin = as.integer(burnin),
mcmc = as.integer(mcmc), thin = as.integer(thin), tune = tune,
lecuyer = as.integer(lecuyer), seedarray = as.integer(seed.array),
lecuyerstream = as.integer(lecuyer.stream), verbose = as.integer(verbose),
betastart = beta.start, betamode = beta.mode, b0 = b0,
B0 = B0, V = V, RW = as.integer(0), tdf = as.double(tdf))
output <- form.mcmc.object(posterior, names = xnames, title = "MCMCmnl Posterior Sample")
} else if (mcmc.method == "slice") {
auto.Scythe.call(output.object = "posterior", cc.fun.name = "MCMCmnlslice",
sample.nonconst = sample, Y = Y, X = X, burnin = as.integer(burnin),
mcmc = as.integer(mcmc), thin = as.integer(thin), lecuyer = as.integer(lecuyer),
seedarray = as.integer(seed.array), lecuyerstream = as.integer(lecuyer.stream),
verbose = as.integer(verbose), betastart = beta.start,
b0 = b0, B0 = B0, V = V)
output <- form.mcmc.object(posterior, names = xnames, title = "MCMCmnl Posterior Sample")
} else {
cat("\n\nmcmc.method not equal to one of 'RWM', 'IndMH', or 'slice'.\n")
stop("Please respecifify and call MCMCmnl() again.\n")
}`: the condition has length > 1
Backtrace:
▆
1. ├─... %>% zelig_qi_to_df() at test-interface.R:74:4
2. ├─Zelig::zelig_qi_to_df(.)
3. │ └─Zelig::is_zelig(obj)
4. ├─Zelig::sim(.)
5. │ └─Zelig::is_zelig(obj)
6. ├─Zelig::setx(.)
7. │ └─Zelig::is_zelig(obj, fail = FALSE)
8. ├─Zelig::zelig(...)
9. │ └─z5$zelig(formula = formula, data = data, ..., by = by)
10. │ └─Zelig callSuper(formula = formula, data = data, ..., by = by, bootstrap = FALSE)
11. │ └─.self$data %>% group_by_(.self$by) %>% ...
12. ├─dplyr::do(., z.out = eval(fn2(.self$model.call, quote(as.data.frame(.)))))
13. ├─dplyr:::do.grouped_df(., z.out = eval(fn2(.self$model.call, quote(as.data.frame(.)))))
14. │ └─rlang::eval_tidy(args[[j]], mask)
15. └─base::eval(fn2(.self$model.call, quote(as.data.frame(.))))
16. └─base::eval(fn2(.self$model.call, quote(as.data.frame(.))))
17. └─MCMCpack::MCMCmnl(...)
[ FAIL 1 | WARN 13 | SKIP 17 | PASS 212 ]
Error: Test failures
Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc
Version: 5.1.7
Check: tests
Result: ERROR
Running 'testthat.R' [220s]
Running the tests in 'tests/testthat.R' failed.
Complete output:
> library(AER)
Loading required package: car
Loading required package: carData
Loading required package: lmtest
Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, as.Date.numeric
Loading required package: sandwich
Loading required package: survival
> library(dplyr)
Attaching package: 'dplyr'
The following object is masked from 'package:car':
recode
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
> library(geepack)
> library(survey)
Loading required package: grid
Loading required package: Matrix
Attaching package: 'survey'
The following object is masked from 'package:graphics':
dotchart
> library(testthat)
Attaching package: 'testthat'
The following object is masked from 'package:dplyr':
matches
>
> set.seed(123)
> test_check("Zelig")
Loading required package: Zelig
-- Imputation 1 --
1 2 3
-- Imputation 2 --
1 2 3
-- Imputation 3 --
1 2 3
-- Imputation 4 --
1 2 3
-- Imputation 5 --
1 2 3
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
-- Imputation 1 --
1 2
-- Imputation 2 --
1 2 3
-- Imputation 3 --
1 2
-- Imputation 4 --
1 2 3
-- Imputation 5 --
1 2
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Ben Goodrich, and Ying Lu. 2013.
normal-bayes: Bayesian Normal Linear Regression
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Andrew D. Martin, Kevin M. Quinn, and Jong Hee Park. 2013.
factor-bayes: Bayesian Factor Analysis
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Terry M. Therneau, and Thomas Lumley. 2011.
exp: Exponential Regression for Duration Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
gamma: Gamma Regression for Continuous, Positive Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
Calculating MLEs and large sample var-cov matrix.
This may take a moment...
Inverting Hessian to get large sample var-cov matrix.
Calculating MLEs and large sample var-cov matrix.
This may take a moment...
Inverting Hessian to get large sample var-cov matrix.
How to cite this model in Zelig:
Christian Kleiber and Achim Zeileis. 2008.
ivreg: Instrumental-Variable Regression
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Christian Kleiber and Achim Zeileis. 2008.
ivreg: Instrumental-Variable Regression
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
Model:
Call:
stats::glm(formula = vote ~ age + race, family = binomial("logit"),
data = as.data.frame(.))
Deviance Residuals:
Min 1Q Median 3Q Max
-1.9268 -1.2962 0.7072 0.7766 1.0723
Coefficients:
Estimate (OR) Std. Error (OR) z value Pr(>|z|)
(Intercept) 1.039111 0.183840 0.217 0.828325
age 1.011327 0.003088 3.689 0.000225 ***
racewhite 1.907038 0.256462 4.800 1.58e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 2266.7 on 1999 degrees of freedom
Residual deviance: 2228.8 on 1997 degrees of freedom
AIC: 2234.8
Number of Fisher Scoring iterations: 4
MCMClogit iteration 1 of 11000
beta =
0.00210
0.98533
Metropolis acceptance rate for beta = 1.00000
MCMClogit iteration 1101 of 11000
beta =
-0.01824
0.93814
Metropolis acceptance rate for beta = 0.52952
MCMClogit iteration 2201 of 11000
beta =
0.00475
0.95312
Metropolis acceptance rate for beta = 0.52158
MCMClogit iteration 3301 of 11000
beta =
-0.01742
0.98950
Metropolis acceptance rate for beta = 0.52802
MCMClogit iteration 4401 of 11000
beta =
-0.03785
0.98980
Metropolis acceptance rate for beta = 0.52602
MCMClogit iteration 5501 of 11000
beta =
-0.04772
0.92064
Metropolis acceptance rate for beta = 0.52209
MCMClogit iteration 6601 of 11000
beta =
0.08646
1.00539
Metropolis acceptance rate for beta = 0.52174
MCMClogit iteration 7701 of 11000
beta =
0.07100
1.01120
Metropolis acceptance rate for beta = 0.52578
MCMClogit iteration 8801 of 11000
beta =
-0.00420
0.88082
Metropolis acceptance rate for beta = 0.52460
MCMClogit iteration 9901 of 11000
beta =
-0.01473
0.87193
Metropolis acceptance rate for beta = 0.52227
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
The Metropolis acceptance rate for beta was 0.52218
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Patrick Lam. 2011.
normal-gee: General Estimating Equation for Normal Regression
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Patrick Lam. 2011.
normal-gee: General Estimating Equation for Normal Regression
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
Model: Combined Imputations
Estimate Std.Error z value Pr(>|z|)
(Intercept) 34.66099 2.83523 12.23 <2e-16
cyl -1.58728 0.49875 -3.18 0.0015
disp -0.02058 0.00696 -2.96 0.0031
For results from individual imputed datasets, use summary(x, subset = i:j)
Next step: Use 'setx' method
MCMCregress iteration 1 of 11000
beta =
0.01119
1.03455
sigma2 = 1.00941
MCMCregress iteration 1101 of 11000
beta =
-0.02594
1.06744
sigma2 = 1.03889
MCMCregress iteration 2201 of 11000
beta =
-0.05222
0.97402
sigma2 = 1.01563
MCMCregress iteration 3301 of 11000
beta =
-0.05933
0.97920
sigma2 = 1.00108
MCMCregress iteration 4401 of 11000
beta =
-0.01337
1.01322
sigma2 = 0.95198
MCMCregress iteration 5501 of 11000
beta =
-0.02224
1.04994
sigma2 = 0.99842
MCMCregress iteration 6601 of 11000
beta =
-0.02043
1.00096
sigma2 = 0.93645
MCMCregress iteration 7701 of 11000
beta =
0.01190
1.06590
sigma2 = 1.01251
MCMCregress iteration 8801 of 11000
beta =
-0.01996
1.01412
sigma2 = 0.95430
MCMCregress iteration 9901 of 11000
beta =
-0.03583
0.97622
sigma2 = 0.99748
How to cite this model in Zelig:
R Core Team. 2007.
poisson: Poisson Regression for Event Count Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
poisson: Poisson Regression for Event Count Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
MCMCpoisson iteration 1 of 11000
beta =
-0.01862
1.02535
Metropolis acceptance rate for beta = 1.00000
MCMCpoisson iteration 1101 of 11000
beta =
-0.00207
1.01122
Metropolis acceptance rate for beta = 0.53224
MCMCpoisson iteration 2201 of 11000
beta =
-0.02369
1.02072
Metropolis acceptance rate for beta = 0.51613
MCMCpoisson iteration 3301 of 11000
beta =
-0.01912
1.02585
Metropolis acceptance rate for beta = 0.51712
MCMCpoisson iteration 4401 of 11000
beta =
-0.05394
1.03940
Metropolis acceptance rate for beta = 0.51602
MCMCpoisson iteration 5501 of 11000
beta =
-0.04054
1.02780
Metropolis acceptance rate for beta = 0.51736
MCMCpoisson iteration 6601 of 11000
beta =
0.08526
0.96386
Metropolis acceptance rate for beta = 0.51750
MCMCpoisson iteration 7701 of 11000
beta =
0.03033
0.99266
Metropolis acceptance rate for beta = 0.52305
MCMCpoisson iteration 8801 of 11000
beta =
-0.00782
1.01038
Metropolis acceptance rate for beta = 0.52324
MCMCpoisson iteration 9901 of 11000
beta =
-0.00920
1.01052
Metropolis acceptance rate for beta = 0.52096
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
The Metropolis acceptance rate for beta was 0.51927
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
How to cite this model in Zelig:
R Core Team. 2007.
probit: Probit Regression for Dichotomous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
MCMCprobit iteration 1 of 11000
beta =
-0.01062
0.93045
MCMCprobit iteration 1101 of 11000
beta =
0.00854
0.98889
MCMCprobit iteration 2201 of 11000
beta =
-0.05915
1.05668
MCMCprobit iteration 3301 of 11000
beta =
-0.01819
0.86660
MCMCprobit iteration 4401 of 11000
beta =
-0.01958
0.95408
MCMCprobit iteration 5501 of 11000
beta =
-0.04281
0.93104
MCMCprobit iteration 6601 of 11000
beta =
-0.04593
0.95252
MCMCprobit iteration 7701 of 11000
beta =
0.01012
1.01175
MCMCprobit iteration 8801 of 11000
beta =
0.01985
1.03090
MCMCprobit iteration 9901 of 11000
beta =
-0.02902
0.99117
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
-- Imputation 1 --
1 2
-- Imputation 2 --
1 2
-- Imputation 3 --
1 2 3
-- Imputation 4 --
1 2 3
-- Imputation 5 --
1 2 3
How to cite this model in Zelig:
Alexander D'Amour. 2008.
quantile: Quantile Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau. 2022.
relogit: Rare Events Logistic Regression for Dichotomous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau. 2022.
relogit: Rare Events Logistic Regression for Dichotomous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau. 2022.
relogit: Rare Events Logistic Regression for Dichotomous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau. 2022.
relogit: Rare Events Logistic Regression for Dichotomous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
Model:
Call:
relogit(formula = cbind(conflict, 1 - conflict) ~ major + contig +
power + maxdem + mindem + years, data = as.data.frame(.),
tau = 0.00343020423212146, bias.correct = TRUE, case.control = "weighting")
Deviance Residuals:
Min 1Q Median 3Q Max
-0.94933 -0.04958 -0.02173 0.19019 0.48253
Coefficients:
Estimate Std. Error (robust) z value Pr(>|z|)
(Intercept) -6.61889 0.31748 -20.848 < 2e-16 ***
major 1.67218 0.27842 6.006 1.9e-09 ***
contig 4.01640 0.22954 17.498 < 2e-16 ***
power 0.28836 0.41574 0.694 0.487925
maxdem 0.06629 0.01925 3.444 0.000573 ***
mindem -0.08143 0.02996 -2.718 0.006572 **
years -0.11707 0.01336 -8.764 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 143.116 on 3125 degrees of freedom
Residual deviance: 91.178 on 3119 degrees of freedom
AIC: 27.041
Number of Fisher Scoring iterations: 10
Model:
Call:
relogit(formula = cbind(conflict, 1 - conflict) ~ major + contig +
power + maxdem + mindem + years, data = as.data.frame(.),
tau = 0.00343020423212146, bias.correct = TRUE, case.control = "weighting")
Deviance Residuals:
Min 1Q Median 3Q Max
-0.94933 -0.04958 -0.02173 0.19019 0.48253
Coefficients:
Estimate Std. Error (robust) z value Pr(>|z|)
(Intercept) -6.61889 0.31748 -20.848 < 2e-16 ***
major 1.67218 0.27842 6.006 1.9e-09 ***
contig 4.01640 0.22954 17.498 < 2e-16 ***
power 0.28836 0.41574 0.694 0.487925
maxdem 0.06629 0.01925 3.444 0.000573 ***
mindem -0.08143 0.02996 -2.718 0.006572 **
years -0.11707 0.01336 -8.764 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 143.116 on 3125 degrees of freedom
Residual deviance: 91.178 on 3119 degrees of freedom
AIC: 27.041
Number of Fisher Scoring iterations: 10
Model:
Call:
relogit(formula = cbind(conflict, 1 - conflict) ~ major + contig +
power + maxdem + mindem + years, data = as.data.frame(.),
tau = 0.00343020423212146, bias.correct = TRUE, case.control = "weighting")
Deviance Residuals:
Min 1Q Median 3Q Max
-0.94933 -0.04958 -0.02173 0.19019 0.48253
Coefficients:
Estimate (OR) Std. Error (OR, robust) z value Pr(>|z|)
(Intercept) 0.001335 0.001521 -20.848 < 2e-16 ***
major 5.323748 4.119262 6.006 1.9e-09 ***
contig 55.501077 41.045844 17.498 < 2e-16 ***
power 1.334239 1.410153 0.694 0.487925
maxdem 1.068533 0.054366 3.444 0.000573 ***
mindem 0.921799 0.067683 -2.718 0.006572 **
years 0.889520 0.041892 -8.764 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 143.116 on 3125 degrees of freedom
Residual deviance: 91.178 on 3119 degrees of freedom
AIC: 27.041
Number of Fisher Scoring iterations: 10
Model:
Call:
z5$zelig(formula = conflict ~ major + contig + power + maxdem +
mindem + years, tau = 1042/303772, case.control = "prior",
data = mid)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.0596 -0.0376 -0.0231 2.1085 4.4649
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -7.525688 0.179685 -41.883 < 2e-16
major 2.433432 0.157561 15.444 < 2e-16
contig 4.112491 0.157650 26.086 < 2e-16
power 1.053747 0.217243 4.851 1.23e-06
maxdem 0.048431 0.010065 4.812 1.50e-06
mindem -0.065249 0.012802 -5.097 3.45e-07
years -0.063359 0.005705 -11.106 < 2e-16
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 3979.5 on 3125 degrees of freedom
Residual deviance: 1868.5 on 3119 degrees of freedom
AIC: 1882.5
Number of Fisher Scoring iterations: 6
Next step: Use 'setx' method
Model:
Call:
`z5$zelig`(formula = conflict ~ major + contig + power + maxdem +
mindem + years, tau = 1042/303772, case.control = "prior",
data = mid)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.0596 -0.0376 -0.0231 2.1085 4.4649
Coefficients:
Estimate (OR) Std. Error (OR) z value Pr(>|z|)
(Intercept) 5.391e-04 9.686e-05 -41.883 < 2e-16 ***
major 1.140e+01 1.796e+00 15.444 < 2e-16 ***
contig 6.110e+01 9.632e+00 26.086 < 2e-16 ***
power 2.868e+00 6.231e-01 4.851 1.23e-06 ***
maxdem 1.050e+00 1.056e-02 4.812 1.50e-06 ***
mindem 9.368e-01 1.199e-02 -5.097 3.45e-07 ***
years 9.386e-01 5.355e-03 -11.106 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 3979.5 on 3125 degrees of freedom
Residual deviance: 1868.5 on 3119 degrees of freedom
AIC: 1882.5
Number of Fisher Scoring iterations: 6
How to cite this model in Zelig:
Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau. 2022.
relogit: Rare Events Logistic Regression for Dichotomous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
MCMCtobit iteration 1 of 11000
beta =
0.09918
0.93061
sigma2 = 0.70553
MCMCtobit iteration 1101 of 11000
beta =
0.07445
0.91668
sigma2 = 0.99194
MCMCtobit iteration 2201 of 11000
beta =
0.00068
0.99780
sigma2 = 0.99760
MCMCtobit iteration 3301 of 11000
beta =
0.04504
0.95894
sigma2 = 0.95844
MCMCtobit iteration 4401 of 11000
beta =
-0.03116
1.00165
sigma2 = 0.97216
MCMCtobit iteration 5501 of 11000
beta =
-0.01771
0.98161
sigma2 = 0.94482
MCMCtobit iteration 6601 of 11000
beta =
-0.00829
0.96500
sigma2 = 0.90195
MCMCtobit iteration 7701 of 11000
beta =
0.09984
0.93619
sigma2 = 1.03124
MCMCtobit iteration 8801 of 11000
beta =
0.03092
0.99268
sigma2 = 0.95955
MCMCtobit iteration 9901 of 11000
beta =
0.04477
0.93956
sigma2 = 1.01930
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
Noninteger weights were set, but the model in Zelig is only able to use integer valued weights.
A bootstrapped version of the dataset was constructed using the weights as sample probabilities.
How to cite this model in Zelig:
R Core Team. 2007.
logit: Logistic Regression for Dichotomous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
logit: Logistic Regression for Dichotomous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
poisson: Poisson Regression for Event Count Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
poisson: Poisson Regression for Event Count Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
setx:
(Intercept) x
1 1 0
setx1:
(Intercept) x
1 1 1
Next step: Use 'sim' method
sim x :
-----
ev
mean sd 50% 2.5% 97.5%
1 -0.000499563 0.006496101 -0.0005420348 -0.01340449 0.01259395
pv
mean sd 50% 2.5% 97.5%
[1,] -0.002512827 0.09819415 -0.003183565 -0.2043797 0.1944359
sim x1 :
-----
ev
mean sd 50% 2.5% 97.5%
1 1.00022 0.006045548 1.000062 0.9887536 1.01215
pv
mean sd 50% 2.5% 97.5%
[1,] 0.9993027 0.09953018 0.9999023 0.8025547 1.197061
fd
mean sd 50% 2.5% 97.5%
1 1.000719 0.01076163 1.000655 0.9799646 1.022849
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
Model: Combined Bootstraps
Estimate Std.Error z value Pr(>|z|)
(Intercept) -0.0003 0.0060 -0.05 0.96
x 1.0006 0.0115 86.67 <2e-16
For results from individual bootstrapped datasets, use summary(x, subset = i:j)
Next step: Use 'setx' method
Bootstrapped Dataset 2
Call:
z5$zelig(formula = formula, data = data, by = by, bootstrap = 20)
Residuals:
Min 1Q Median 3Q Max
-0.09973 -0.09750 -0.09523 0.10254 0.10486
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.004872 0.006185 -0.788 0.431
x 1.004608 0.010780 93.194 <2e-16
Residual standard error: 0.1001 on 998 degrees of freedom
Multiple R-squared: 0.8969, Adjusted R-squared: 0.8968
F-statistic: 8685 on 1 and 998 DF, p-value: < 2.2e-16
Bootstrapped Dataset 3
Call:
z5$zelig(formula = formula, data = data, by = by, bootstrap = 20)
Residuals:
Min 1Q Median 3Q Max
-0.10360 -0.09514 -0.08812 0.10411 0.11242
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.003619 0.006352 0.57 0.569
x 0.983959 0.010756 91.48 <2e-16
Residual standard error: 0.09988 on 998 degrees of freedom
Multiple R-squared: 0.8934, Adjusted R-squared: 0.8933
F-statistic: 8368 on 1 and 998 DF, p-value: < 2.2e-16
Next step: Use 'setx' method
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
Model: Combined Imputations
Estimate Std.Error z value Pr(>|z|)
(Intercept) -1.001 1.733 -0.58 0.56
x 1.001 0.011 91.25 <2e-16
For results from individual imputed datasets, use summary(x, subset = i:j)
Next step: Use 'setx' method
Imputed Dataset 1
Call:
z5$zelig(formula = formula, data = data, by = by)
Residuals:
Min 1Q Median 3Q Max
-0.1003 -0.1000 0.0000 0.1000 0.1003
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.0003003 0.0063356 -0.047 0.962
x 1.0006000 0.0109654 91.251 <2e-16
Residual standard error: 0.1001 on 998 degrees of freedom
Multiple R-squared: 0.893, Adjusted R-squared: 0.8929
F-statistic: 8327 on 1 and 998 DF, p-value: < 2.2e-16
Next step: Use 'setx' method
Model:
$rr
[1] 0
Call:
zb.out$zelig(formula = yb ~ xx + zz, data = data, by = "rr")
Deviance Residuals:
Min 1Q Median 3Q Max
-2.6031 0.2531 0.3125 0.3947 0.6564
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.274 1.045 1.219 0.223
xx 1.088 1.195 0.910 0.363
zz 1.975 2.152 0.918 0.359
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 28.820 on 55 degrees of freedom
Residual deviance: 26.896 on 53 degrees of freedom
AIC: 32.896
Number of Fisher Scoring iterations: 6
Model:
$rr
[1] 1
Call:
zb.out$zelig(formula = yb ~ xx + zz, data = data, by = "rr")
Deviance Residuals:
Min 1Q Median 3Q Max
-2.6982 0.2266 0.3859 0.7298 1.0780
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.2255 1.0561 -0.214 0.831
xx 2.2702 1.1632 1.952 0.051
zz 2.1285 1.8878 1.128 0.260
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 41.724 on 43 degrees of freedom
Residual deviance: 35.553 on 41 degrees of freedom
AIC: 41.553
Number of Fisher Scoring iterations: 5
Next step: Use 'setx' method
sim x :
-----
ev
mean sd 50% 2.5% 97.5%
[1,] 0.9476733 0.05386777 0.9665833 0.8009766 0.9955033
pv
0 1
[1,] 0.058 0.942
sim x :
-----
ev
mean sd 50% 2.5% 97.5%
[1,] 0.9367684 0.07021485 0.9611249 0.7428204 0.9949634
pv
0 1
[1,] 0.061 0.939
sim x1 :
-----
ev
mean sd 50% 2.5% 97.5%
[1,] 0.8918403 0.06422275 0.9049403 0.7298034 0.9740786
pv
0 1
[1,] 0.101 0.899
fd
mean sd 50% 2.5% 97.5%
[1,] -0.05583303 0.07977401 -0.05088306 -0.2221262 0.1014462
sim x1 :
-----
ev
mean sd 50% 2.5% 97.5%
[1,] 0.6990619 0.09467077 0.7081549 0.5028567 0.8603348
pv
0 1
[1,] 0.31 0.69
fd
mean sd 50% 2.5% 97.5%
[1,] -0.2377065 0.1148547 -0.2380952 -0.466211 -0.01659926
[ FAIL 1 | WARN 13 | SKIP 17 | PASS 212 ]
══ Skipped tests ═══════════════════════════════════════════════════════════════
• On CRAN (17)
══ Failed tests ════════════════════════════════════════════════════════════════
── Error (test-interface.R:74:5): REQUIRE TEST zelig_qi_to_df multinomial outcome ──
Error in `if (mcmc.method == "RWM") {
auto.Scythe.call(output.object = "posterior", cc.fun.name = "MCMCmnlMH",
sample.nonconst = sample, Y = Y, X = X, burnin = as.integer(burnin),
mcmc = as.integer(mcmc), thin = as.integer(thin), tune = tune,
lecuyer = as.integer(lecuyer), seedarray = as.integer(seed.array),
lecuyerstream = as.integer(lecuyer.stream), verbose = as.integer(verbose),
betastart = beta.start, betamode = beta.mode, b0 = b0,
B0 = B0, V = V, RW = as.integer(1), tdf = as.double(tdf))
output <- form.mcmc.object(posterior, names = xnames, title = "MCMCmnl Posterior Sample")
} else if (mcmc.method == "IndMH") {
auto.Scythe.call(output.object = "posterior", cc.fun.name = "MCMCmnlMH",
sample.nonconst = sample, Y = Y, X = X, burnin = as.integer(burnin),
mcmc = as.integer(mcmc), thin = as.integer(thin), tune = tune,
lecuyer = as.integer(lecuyer), seedarray = as.integer(seed.array),
lecuyerstream = as.integer(lecuyer.stream), verbose = as.integer(verbose),
betastart = beta.start, betamode = beta.mode, b0 = b0,
B0 = B0, V = V, RW = as.integer(0), tdf = as.double(tdf))
output <- form.mcmc.object(posterior, names = xnames, title = "MCMCmnl Posterior Sample")
} else if (mcmc.method == "slice") {
auto.Scythe.call(output.object = "posterior", cc.fun.name = "MCMCmnlslice",
sample.nonconst = sample, Y = Y, X = X, burnin = as.integer(burnin),
mcmc = as.integer(mcmc), thin = as.integer(thin), lecuyer = as.integer(lecuyer),
seedarray = as.integer(seed.array), lecuyerstream = as.integer(lecuyer.stream),
verbose = as.integer(verbose), betastart = beta.start,
b0 = b0, B0 = B0, V = V)
output <- form.mcmc.object(posterior, names = xnames, title = "MCMCmnl Posterior Sample")
} else {
cat("\n\nmcmc.method not equal to one of 'RWM', 'IndMH', or 'slice'.\n")
stop("Please respecifify and call MCMCmnl() again.\n")
}`: the condition has length > 1
Backtrace:
▆
1. ├─... %>% zelig_qi_to_df() at test-interface.R:74:4
2. ├─Zelig::zelig_qi_to_df(.)
3. │ └─Zelig::is_zelig(obj)
4. ├─Zelig::sim(.)
5. │ └─Zelig::is_zelig(obj)
6. ├─Zelig::setx(.)
7. │ └─Zelig::is_zelig(obj, fail = FALSE)
8. ├─Zelig::zelig(...)
9. │ └─z5$zelig(formula = formula, data = data, ..., by = by)
10. │ └─Zelig callSuper(formula = formula, data = data, ..., by = by, bootstrap = FALSE)
11. │ └─.self$data %>% group_by_(.self$by) %>% ...
12. ├─dplyr::do(., z.out = eval(fn2(.self$model.call, quote(as.data.frame(.)))))
13. ├─dplyr:::do.grouped_df(., z.out = eval(fn2(.self$model.call, quote(as.data.frame(.)))))
14. │ └─rlang::eval_tidy(args[[j]], mask)
15. └─base::eval(fn2(.self$model.call, quote(as.data.frame(.))))
16. └─base::eval(fn2(.self$model.call, quote(as.data.frame(.))))
17. └─MCMCpack::MCMCmnl(...)
[ FAIL 1 | WARN 13 | SKIP 17 | PASS 212 ]
Error: Test failures
Execution halted
Flavor: r-devel-windows-x86_64-new-UL
Version: 5.1.7
Check: tests
Result: ERROR
Running 'testthat.R'
Running the tests in 'tests/testthat.R' failed.
Complete output:
> library(AER)
Loading required package: car
Loading required package: carData
Loading required package: lmtest
Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, as.Date.numeric
Loading required package: sandwich
Loading required package: survival
> library(dplyr)
Attaching package: 'dplyr'
The following object is masked from 'package:car':
recode
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
> library(geepack)
> library(survey)
Loading required package: grid
Loading required package: Matrix
Attaching package: 'survey'
The following object is masked from 'package:graphics':
dotchart
> library(testthat)
Attaching package: 'testthat'
The following object is masked from 'package:dplyr':
matches
>
> set.seed(123)
> test_check("Zelig")
Loading required package: Zelig
-- Imputation 1 --
1 2 3
-- Imputation 2 --
1 2 3
-- Imputation 3 --
1 2 3
-- Imputation 4 --
1 2 3
-- Imputation 5 --
1 2 3
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
-- Imputation 1 --
1 2
-- Imputation 2 --
1 2 3
-- Imputation 3 --
1 2
-- Imputation 4 --
1 2 3
-- Imputation 5 --
1 2
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Ben Goodrich, and Ying Lu. 2013.
normal-bayes: Bayesian Normal Linear Regression
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Andrew D. Martin, Kevin M. Quinn, and Jong Hee Park. 2013.
factor-bayes: Bayesian Factor Analysis
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Terry M. Therneau, and Thomas Lumley. 2011.
exp: Exponential Regression for Duration Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
gamma: Gamma Regression for Continuous, Positive Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
Calculating MLEs and large sample var-cov matrix.
This may take a moment...
Inverting Hessian to get large sample var-cov matrix.
Calculating MLEs and large sample var-cov matrix.
This may take a moment...
Inverting Hessian to get large sample var-cov matrix.
How to cite this model in Zelig:
Christian Kleiber and Achim Zeileis. 2008.
ivreg: Instrumental-Variable Regression
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Christian Kleiber and Achim Zeileis. 2008.
ivreg: Instrumental-Variable Regression
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
Model:
Call:
stats::glm(formula = vote ~ age + race, family = binomial("logit"),
data = as.data.frame(.))
Deviance Residuals:
Min 1Q Median 3Q Max
-1.9268 -1.2962 0.7072 0.7766 1.0723
Coefficients:
Estimate (OR) Std. Error (OR) z value Pr(>|z|)
(Intercept) 1.039111 0.183840 0.217 0.828325
age 1.011327 0.003088 3.689 0.000225 ***
racewhite 1.907038 0.256462 4.800 1.58e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 2266.7 on 1999 degrees of freedom
Residual deviance: 2228.8 on 1997 degrees of freedom
AIC: 2234.8
Number of Fisher Scoring iterations: 4
MCMClogit iteration 1 of 11000
beta =
0.00210
0.98533
Metropolis acceptance rate for beta = 1.00000
MCMClogit iteration 1101 of 11000
beta =
-0.01824
0.93814
Metropolis acceptance rate for beta = 0.52952
MCMClogit iteration 2201 of 11000
beta =
0.00475
0.95312
Metropolis acceptance rate for beta = 0.52158
MCMClogit iteration 3301 of 11000
beta =
-0.01742
0.98950
Metropolis acceptance rate for beta = 0.52802
MCMClogit iteration 4401 of 11000
beta =
-0.03785
0.98980
Metropolis acceptance rate for beta = 0.52602
MCMClogit iteration 5501 of 11000
beta =
-0.04772
0.92064
Metropolis acceptance rate for beta = 0.52209
MCMClogit iteration 6601 of 11000
beta =
0.08646
1.00539
Metropolis acceptance rate for beta = 0.52174
MCMClogit iteration 7701 of 11000
beta =
0.07100
1.01120
Metropolis acceptance rate for beta = 0.52578
MCMClogit iteration 8801 of 11000
beta =
-0.00420
0.88082
Metropolis acceptance rate for beta = 0.52460
MCMClogit iteration 9901 of 11000
beta =
-0.01473
0.87193
Metropolis acceptance rate for beta = 0.52227
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
The Metropolis acceptance rate for beta was 0.52218
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Patrick Lam. 2011.
normal-gee: General Estimating Equation for Normal Regression
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Patrick Lam. 2011.
normal-gee: General Estimating Equation for Normal Regression
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
Model: Combined Imputations
Estimate Std.Error z value Pr(>|z|)
(Intercept) 34.66099 2.83523 12.23 <2e-16
cyl -1.58728 0.49875 -3.18 0.0015
disp -0.02058 0.00696 -2.96 0.0031
For results from individual imputed datasets, use summary(x, subset = i:j)
Next step: Use 'setx' method
MCMCregress iteration 1 of 11000
beta =
0.01119
1.03455
sigma2 = 1.00941
MCMCregress iteration 1101 of 11000
beta =
-0.02594
1.06744
sigma2 = 1.03889
MCMCregress iteration 2201 of 11000
beta =
-0.05222
0.97402
sigma2 = 1.01563
MCMCregress iteration 3301 of 11000
beta =
-0.05933
0.97920
sigma2 = 1.00108
MCMCregress iteration 4401 of 11000
beta =
-0.01337
1.01322
sigma2 = 0.95198
MCMCregress iteration 5501 of 11000
beta =
-0.02224
1.04994
sigma2 = 0.99842
MCMCregress iteration 6601 of 11000
beta =
-0.02043
1.00096
sigma2 = 0.93645
MCMCregress iteration 7701 of 11000
beta =
0.01190
1.06590
sigma2 = 1.01251
MCMCregress iteration 8801 of 11000
beta =
-0.01996
1.01412
sigma2 = 0.95430
MCMCregress iteration 9901 of 11000
beta =
-0.03583
0.97622
sigma2 = 0.99748
How to cite this model in Zelig:
R Core Team. 2007.
poisson: Poisson Regression for Event Count Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
poisson: Poisson Regression for Event Count Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
MCMCpoisson iteration 1 of 11000
beta =
-0.01862
1.02535
Metropolis acceptance rate for beta = 1.00000
MCMCpoisson iteration 1101 of 11000
beta =
-0.00207
1.01122
Metropolis acceptance rate for beta = 0.53224
MCMCpoisson iteration 2201 of 11000
beta =
-0.02369
1.02072
Metropolis acceptance rate for beta = 0.51613
MCMCpoisson iteration 3301 of 11000
beta =
-0.01912
1.02585
Metropolis acceptance rate for beta = 0.51712
MCMCpoisson iteration 4401 of 11000
beta =
-0.05394
1.03940
Metropolis acceptance rate for beta = 0.51602
MCMCpoisson iteration 5501 of 11000
beta =
-0.04054
1.02780
Metropolis acceptance rate for beta = 0.51736
MCMCpoisson iteration 6601 of 11000
beta =
0.08526
0.96386
Metropolis acceptance rate for beta = 0.51750
MCMCpoisson iteration 7701 of 11000
beta =
0.03033
0.99266
Metropolis acceptance rate for beta = 0.52305
MCMCpoisson iteration 8801 of 11000
beta =
-0.00782
1.01038
Metropolis acceptance rate for beta = 0.52324
MCMCpoisson iteration 9901 of 11000
beta =
-0.00920
1.01052
Metropolis acceptance rate for beta = 0.52096
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
The Metropolis acceptance rate for beta was 0.51927
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
How to cite this model in Zelig:
R Core Team. 2007.
probit: Probit Regression for Dichotomous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
MCMCprobit iteration 1 of 11000
beta =
-0.01062
0.93045
MCMCprobit iteration 1101 of 11000
beta =
0.00854
0.98889
MCMCprobit iteration 2201 of 11000
beta =
-0.05915
1.05668
MCMCprobit iteration 3301 of 11000
beta =
-0.01819
0.86660
MCMCprobit iteration 4401 of 11000
beta =
-0.01958
0.95408
MCMCprobit iteration 5501 of 11000
beta =
-0.04281
0.93104
MCMCprobit iteration 6601 of 11000
beta =
-0.04593
0.95252
MCMCprobit iteration 7701 of 11000
beta =
0.01012
1.01175
MCMCprobit iteration 8801 of 11000
beta =
0.01985
1.03090
MCMCprobit iteration 9901 of 11000
beta =
-0.02902
0.99117
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
-- Imputation 1 --
1 2
-- Imputation 2 --
1 2
-- Imputation 3 --
1 2 3
-- Imputation 4 --
1 2 3
-- Imputation 5 --
1 2 3
How to cite this model in Zelig:
Alexander D'Amour. 2008.
quantile: Quantile Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau. 2022.
relogit: Rare Events Logistic Regression for Dichotomous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau. 2022.
relogit: Rare Events Logistic Regression for Dichotomous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau. 2022.
relogit: Rare Events Logistic Regression for Dichotomous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau. 2022.
relogit: Rare Events Logistic Regression for Dichotomous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
Model:
Call:
relogit(formula = cbind(conflict, 1 - conflict) ~ major + contig +
power + maxdem + mindem + years, data = as.data.frame(.),
tau = 0.00343020423212146, bias.correct = TRUE, case.control = "weighting")
Deviance Residuals:
Min 1Q Median 3Q Max
-0.94933 -0.04958 -0.02173 0.19019 0.48253
Coefficients:
Estimate Std. Error (robust) z value Pr(>|z|)
(Intercept) -6.61889 0.31748 -20.848 < 2e-16 ***
major 1.67218 0.27842 6.006 1.9e-09 ***
contig 4.01640 0.22954 17.498 < 2e-16 ***
power 0.28836 0.41574 0.694 0.487925
maxdem 0.06629 0.01925 3.444 0.000573 ***
mindem -0.08143 0.02996 -2.718 0.006572 **
years -0.11707 0.01336 -8.764 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 143.116 on 3125 degrees of freedom
Residual deviance: 91.178 on 3119 degrees of freedom
AIC: 27.041
Number of Fisher Scoring iterations: 10
Model:
Call:
relogit(formula = cbind(conflict, 1 - conflict) ~ major + contig +
power + maxdem + mindem + years, data = as.data.frame(.),
tau = 0.00343020423212146, bias.correct = TRUE, case.control = "weighting")
Deviance Residuals:
Min 1Q Median 3Q Max
-0.94933 -0.04958 -0.02173 0.19019 0.48253
Coefficients:
Estimate Std. Error (robust) z value Pr(>|z|)
(Intercept) -6.61889 0.31748 -20.848 < 2e-16 ***
major 1.67218 0.27842 6.006 1.9e-09 ***
contig 4.01640 0.22954 17.498 < 2e-16 ***
power 0.28836 0.41574 0.694 0.487925
maxdem 0.06629 0.01925 3.444 0.000573 ***
mindem -0.08143 0.02996 -2.718 0.006572 **
years -0.11707 0.01336 -8.764 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 143.116 on 3125 degrees of freedom
Residual deviance: 91.178 on 3119 degrees of freedom
AIC: 27.041
Number of Fisher Scoring iterations: 10
Model:
Call:
relogit(formula = cbind(conflict, 1 - conflict) ~ major + contig +
power + maxdem + mindem + years, data = as.data.frame(.),
tau = 0.00343020423212146, bias.correct = TRUE, case.control = "weighting")
Deviance Residuals:
Min 1Q Median 3Q Max
-0.94933 -0.04958 -0.02173 0.19019 0.48253
Coefficients:
Estimate (OR) Std. Error (OR, robust) z value Pr(>|z|)
(Intercept) 0.001335 0.001521 -20.848 < 2e-16 ***
major 5.323748 4.119262 6.006 1.9e-09 ***
contig 55.501077 41.045844 17.498 < 2e-16 ***
power 1.334239 1.410153 0.694 0.487925
maxdem 1.068533 0.054366 3.444 0.000573 ***
mindem 0.921799 0.067683 -2.718 0.006572 **
years 0.889520 0.041892 -8.764 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 143.116 on 3125 degrees of freedom
Residual deviance: 91.178 on 3119 degrees of freedom
AIC: 27.041
Number of Fisher Scoring iterations: 10
Model:
Call:
z5$zelig(formula = conflict ~ major + contig + power + maxdem +
mindem + years, tau = 1042/303772, case.control = "prior",
data = mid)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.0596 -0.0376 -0.0231 2.1085 4.4649
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -7.525688 0.179685 -41.883 < 2e-16
major 2.433432 0.157561 15.444 < 2e-16
contig 4.112491 0.157650 26.086 < 2e-16
power 1.053747 0.217243 4.851 1.23e-06
maxdem 0.048431 0.010065 4.812 1.50e-06
mindem -0.065249 0.012802 -5.097 3.45e-07
years -0.063359 0.005705 -11.106 < 2e-16
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 3979.5 on 3125 degrees of freedom
Residual deviance: 1868.5 on 3119 degrees of freedom
AIC: 1882.5
Number of Fisher Scoring iterations: 6
Next step: Use 'setx' method
Model:
Call:
`z5$zelig`(formula = conflict ~ major + contig + power + maxdem +
mindem + years, tau = 1042/303772, case.control = "prior",
data = mid)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.0596 -0.0376 -0.0231 2.1085 4.4649
Coefficients:
Estimate (OR) Std. Error (OR) z value Pr(>|z|)
(Intercept) 5.391e-04 9.686e-05 -41.883 < 2e-16 ***
major 1.140e+01 1.796e+00 15.444 < 2e-16 ***
contig 6.110e+01 9.632e+00 26.086 < 2e-16 ***
power 2.868e+00 6.231e-01 4.851 1.23e-06 ***
maxdem 1.050e+00 1.056e-02 4.812 1.50e-06 ***
mindem 9.368e-01 1.199e-02 -5.097 3.45e-07 ***
years 9.386e-01 5.355e-03 -11.106 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 3979.5 on 3125 degrees of freedom
Residual deviance: 1868.5 on 3119 degrees of freedom
AIC: 1882.5
Number of Fisher Scoring iterations: 6
How to cite this model in Zelig:
Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau. 2022.
relogit: Rare Events Logistic Regression for Dichotomous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
MCMCtobit iteration 1 of 11000
beta =
0.09918
0.93061
sigma2 = 0.70553
MCMCtobit iteration 1101 of 11000
beta =
0.07445
0.91668
sigma2 = 0.99194
MCMCtobit iteration 2201 of 11000
beta =
0.00068
0.99780
sigma2 = 0.99760
MCMCtobit iteration 3301 of 11000
beta =
0.04504
0.95894
sigma2 = 0.95844
MCMCtobit iteration 4401 of 11000
beta =
-0.03116
1.00165
sigma2 = 0.97216
MCMCtobit iteration 5501 of 11000
beta =
-0.01771
0.98161
sigma2 = 0.94482
MCMCtobit iteration 6601 of 11000
beta =
-0.00829
0.96500
sigma2 = 0.90195
MCMCtobit iteration 7701 of 11000
beta =
0.09984
0.93619
sigma2 = 1.03124
MCMCtobit iteration 8801 of 11000
beta =
0.03092
0.99268
sigma2 = 0.95955
MCMCtobit iteration 9901 of 11000
beta =
0.04477
0.93956
sigma2 = 1.01930
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
Noninteger weights were set, but the model in Zelig is only able to use integer valued weights.
A bootstrapped version of the dataset was constructed using the weights as sample probabilities.
How to cite this model in Zelig:
R Core Team. 2007.
logit: Logistic Regression for Dichotomous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
logit: Logistic Regression for Dichotomous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
poisson: Poisson Regression for Event Count Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
poisson: Poisson Regression for Event Count Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
setx:
(Intercept) x
1 1 0
setx1:
(Intercept) x
1 1 1
Next step: Use 'sim' method
sim x :
-----
ev
mean sd 50% 2.5% 97.5%
1 -0.000499563 0.006496101 -0.0005420348 -0.01340449 0.01259395
pv
mean sd 50% 2.5% 97.5%
[1,] -0.002512827 0.09819415 -0.003183565 -0.2043797 0.1944359
sim x1 :
-----
ev
mean sd 50% 2.5% 97.5%
1 1.00022 0.006045548 1.000062 0.9887536 1.01215
pv
mean sd 50% 2.5% 97.5%
[1,] 0.9993027 0.09953018 0.9999023 0.8025547 1.197061
fd
mean sd 50% 2.5% 97.5%
1 1.000719 0.01076163 1.000655 0.9799646 1.022849
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
Model: Combined Bootstraps
Estimate Std.Error z value Pr(>|z|)
(Intercept) -0.0003 0.0060 -0.05 0.96
x 1.0006 0.0115 86.67 <2e-16
For results from individual bootstrapped datasets, use summary(x, subset = i:j)
Next step: Use 'setx' method
Bootstrapped Dataset 2
Call:
z5$zelig(formula = formula, data = data, by = by, bootstrap = 20)
Residuals:
Min 1Q Median 3Q Max
-0.09973 -0.09750 -0.09523 0.10254 0.10486
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.004872 0.006185 -0.788 0.431
x 1.004608 0.010780 93.194 <2e-16
Residual standard error: 0.1001 on 998 degrees of freedom
Multiple R-squared: 0.8969, Adjusted R-squared: 0.8968
F-statistic: 8685 on 1 and 998 DF, p-value: < 2.2e-16
Bootstrapped Dataset 3
Call:
z5$zelig(formula = formula, data = data, by = by, bootstrap = 20)
Residuals:
Min 1Q Median 3Q Max
-0.10360 -0.09514 -0.08812 0.10411 0.11242
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.003619 0.006352 0.57 0.569
x 0.983959 0.010756 91.48 <2e-16
Residual standard error: 0.09988 on 998 degrees of freedom
Multiple R-squared: 0.8934, Adjusted R-squared: 0.8933
F-statistic: 8368 on 1 and 998 DF, p-value: < 2.2e-16
Next step: Use 'setx' method
How to cite this model in Zelig:
R Core Team. 2007.
ls: Least Squares Regression for Continuous Dependent Variables
in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
"Zelig: Everyone's Statistical Software," https://zeligproject.org/
Model: Combined Imputations
Estimate Std.Error z value Pr(>|z|)
(Intercept) -1.001 1.733 -0.58 0.56
x 1.001 0.011 91.25 <2e-16
For results from individual imputed datasets, use summary(x, subset = i:j)
Next step: Use 'setx' method
Imputed Dataset 1
Call:
z5$zelig(formula = formula, data = data, by = by)
Residuals:
Min 1Q Median 3Q Max
-0.1003 -0.1000 0.0000 0.1000 0.1003
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.0003003 0.0063356 -0.047 0.962
x 1.0006000 0.0109654 91.251 <2e-16
Residual standard error: 0.1001 on 998 degrees of freedom
Multiple R-squared: 0.893, Adjusted R-squared: 0.8929
F-statistic: 8327 on 1 and 998 DF, p-value: < 2.2e-16
Next step: Use 'setx' method
Model:
$rr
[1] 0
Call:
zb.out$zelig(formula = yb ~ xx + zz, data = data, by = "rr")
Deviance Residuals:
Min 1Q Median 3Q Max
-2.6031 0.2531 0.3125 0.3947 0.6564
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.274 1.045 1.219 0.223
xx 1.088 1.195 0.910 0.363
zz 1.975 2.152 0.918 0.359
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 28.820 on 55 degrees of freedom
Residual deviance: 26.896 on 53 degrees of freedom
AIC: 32.896
Number of Fisher Scoring iterations: 6
Model:
$rr
[1] 1
Call:
zb.out$zelig(formula = yb ~ xx + zz, data = data, by = "rr")
Deviance Residuals:
Min 1Q Median 3Q Max
-2.6982 0.2266 0.3859 0.7298 1.0780
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.2255 1.0561 -0.214 0.831
xx 2.2702 1.1632 1.952 0.051
zz 2.1285 1.8878 1.128 0.260
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 41.724 on 43 degrees of freedom
Residual deviance: 35.553 on 41 degrees of freedom
AIC: 41.553
Number of Fisher Scoring iterations: 5
Next step: Use 'setx' method
sim x :
-----
ev
mean sd 50% 2.5% 97.5%
[1,] 0.9476733 0.05386777 0.9665833 0.8009766 0.9955033
pv
0 1
[1,] 0.058 0.942
sim x :
-----
ev
mean sd 50% 2.5% 97.5%
[1,] 0.9367684 0.07021485 0.9611249 0.7428204 0.9949634
pv
0 1
[1,] 0.061 0.939
sim x1 :
-----
ev
mean sd 50% 2.5% 97.5%
[1,] 0.8918403 0.06422275 0.9049403 0.7298034 0.9740786
pv
0 1
[1,] 0.101 0.899
fd
mean sd 50% 2.5% 97.5%
[1,] -0.05583303 0.07977401 -0.05088306 -0.2221262 0.1014462
sim x1 :
-----
ev
mean sd 50% 2.5% 97.5%
[1,] 0.6990619 0.09467077 0.7081549 0.5028567 0.8603348
pv
0 1
[1,] 0.31 0.69
fd
mean sd 50% 2.5% 97.5%
[1,] -0.2377065 0.1148547 -0.2380952 -0.466211 -0.01659926
[ FAIL 1 | WARN 13 | SKIP 17 | PASS 212 ]
══ Skipped tests ═══════════════════════════════════════════════════════════════
• On CRAN (17)
══ Failed tests ════════════════════════════════════════════════════════════════
── Error (test-interface.R:74:5): REQUIRE TEST zelig_qi_to_df multinomial outcome ──
Error in `if (mcmc.method == "RWM") {
auto.Scythe.call(output.object = "posterior", cc.fun.name = "MCMCmnlMH",
sample.nonconst = sample, Y = Y, X = X, burnin = as.integer(burnin),
mcmc = as.integer(mcmc), thin = as.integer(thin), tune = tune,
lecuyer = as.integer(lecuyer), seedarray = as.integer(seed.array),
lecuyerstream = as.integer(lecuyer.stream), verbose = as.integer(verbose),
betastart = beta.start, betamode = beta.mode, b0 = b0,
B0 = B0, V = V, RW = as.integer(1), tdf = as.double(tdf))
output <- form.mcmc.object(posterior, names = xnames, title = "MCMCmnl Posterior Sample")
} else if (mcmc.method == "IndMH") {
auto.Scythe.call(output.object = "posterior", cc.fun.name = "MCMCmnlMH",
sample.nonconst = sample, Y = Y, X = X, burnin = as.integer(burnin),
mcmc = as.integer(mcmc), thin = as.integer(thin), tune = tune,
lecuyer = as.integer(lecuyer), seedarray = as.integer(seed.array),
lecuyerstream = as.integer(lecuyer.stream), verbose = as.integer(verbose),
betastart = beta.start, betamode = beta.mode, b0 = b0,
B0 = B0, V = V, RW = as.integer(0), tdf = as.double(tdf))
output <- form.mcmc.object(posterior, names = xnames, title = "MCMCmnl Posterior Sample")
} else if (mcmc.method == "slice") {
auto.Scythe.call(output.object = "posterior", cc.fun.name = "MCMCmnlslice",
sample.nonconst = sample, Y = Y, X = X, burnin = as.integer(burnin),
mcmc = as.integer(mcmc), thin = as.integer(thin), lecuyer = as.integer(lecuyer),
seedarray = as.integer(seed.array), lecuyerstream = as.integer(lecuyer.stream),
verbose = as.integer(verbose), betastart = beta.start,
b0 = b0, B0 = B0, V = V)
output <- form.mcmc.object(posterior, names = xnames, title = "MCMCmnl Posterior Sample")
} else {
cat("\n\nmcmc.method not equal to one of 'RWM', 'IndMH', or 'slice'.\n")
stop("Please respecifify and call MCMCmnl() again.\n")
}`: the condition has length > 1
Backtrace:
▆
1. ├─... %>% zelig_qi_to_df() at test-interface.R:74:4
2. ├─Zelig::zelig_qi_to_df(.)
3. │ └─Zelig::is_zelig(obj)
4. ├─Zelig::sim(.)
5. │ └─Zelig::is_zelig(obj)
6. ├─Zelig::setx(.)
7. │ └─Zelig::is_zelig(obj, fail = FALSE)
8. ├─Zelig::zelig(...)
9. │ └─z5$zelig(formula = formula, data = data, ..., by = by)
10. │ └─Zelig callSuper(formula = formula, data = data, ..., by = by, bootstrap = FALSE)
11. │ └─.self$data %>% group_by_(.self$by) %>% ...
12. ├─dplyr::do(., z.out = eval(fn2(.self$model.call, quote(as.data.frame(.)))))
13. ├─dplyr:::do.grouped_df(., z.out = eval(fn2(.self$model.call, quote(as.data.frame(.)))))
14. │ └─rlang::eval_tidy(args[[j]], mask)
15. └─base::eval(fn2(.self$model.call, quote(as.data.frame(.))))
16. └─base::eval(fn2(.self$model.call, quote(as.data.frame(.))))
17. └─MCMCpack::MCMCmnl(...)
[ FAIL 1 | WARN 13 | SKIP 17 | PASS 212 ]
Error: Test failures
Execution halted
Flavor: r-devel-windows-x86_64-new-TK