Last updated on 2023-04-13 06:56:44 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 0.6-7 | 9.20 | 109.05 | 118.25 | ERROR | |
| r-devel-linux-x86_64-debian-gcc | 0.6-7 | 8.83 | 84.47 | 93.30 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 0.6-7 | 149.87 | ERROR | |||
| r-devel-linux-x86_64-fedora-gcc | 0.6-7 | 158.94 | ERROR | |||
| r-devel-macos-arm64 | 0.6-7 | 57.00 | ERROR | |||
| r-devel-macos-x86_64 | 0.6-7 | 102.00 | ERROR | |||
| r-devel-windows-x86_64 | 0.6-7 | 15.00 | 161.00 | 176.00 | ERROR | |
| r-patched-linux-x86_64 | 0.6-7 | 16.00 | 105.21 | 121.21 | ERROR | |
| r-release-linux-x86_64 | 0.6-7 | 10.25 | 101.64 | 111.89 | ERROR | |
| r-release-macos-arm64 | 0.6-7 | 50.00 | OK | |||
| r-release-macos-x86_64 | 0.6-7 | 81.00 | OK | |||
| r-release-windows-x86_64 | 0.6-7 | 14.00 | 155.00 | 169.00 | ERROR | |
| r-oldrel-macos-arm64 | 0.6-7 | 72.00 | OK | |||
| r-oldrel-macos-x86_64 | 0.6-7 | 112.00 | OK | |||
| r-oldrel-windows-ix86+x86_64 | 0.6-7 | 24.00 | 141.00 | 165.00 | ERROR |
Version: 0.6-7
Check: S3 generic/method consistency
Result: WARN
residualPlots:
function(model, ...)
residualPlots.expData:
function(object, ...)
residualPlots:
function(model, ...)
residualPlots.neModel:
function(object, ...)
residualPlot:
function(model, ...)
residualPlot.neModel:
function(object, ...)
residualPlot:
function(model, ...)
residualPlot.expData:
function(object, ...)
See section ‘Generic functions and methods’ in the ‘Writing R
Extensions’ manual.
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-macos-arm64, r-devel-macos-x86_64, r-devel-windows-x86_64, r-patched-linux-x86_64
Version: 0.6-7
Check: examples
Result: ERROR
Running examples in ‘medflex-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: neModel-methods
> ### Title: Methods for natural effect models
> ### Aliases: neModel-methods coef.neModel confint.neModelBoot
> ### confint.neModel residualPlot.neModel residualPlots.neModel
> ### summary.neModel vcov.neModel weights.neModel
>
> ### ** Examples
>
> data(UPBdata)
>
> weightData <- neWeight(negaff ~ att + educ + gender + age,
+ data = UPBdata)
> neMod <- neModel(UPB ~ att0 * att1 + educ + gender + age,
+ family = binomial, expData = weightData, se = "robust")
>
> ## extract coefficients
> coef(neMod)
(Intercept) att0 att1 educM educH genderM
-0.52588124 0.29321882 0.17465629 0.19695736 0.39686487 0.33060892
age att0:att1
-0.01271054 0.07992366
>
> ## extract variance-covariance matrix
> vcov(neMod)
(Intercept) att0 att1 educM
(Intercept) 0.5107962853 1.205653e-04 -9.948131e-04 -0.2101703627
att0 0.0001205653 1.435228e-02 -1.692869e-03 0.0025776663
att1 -0.0009948131 -1.692869e-03 2.330610e-03 -0.0011022216
educM -0.2101703627 2.577666e-03 -1.102222e-03 0.2464137181
educH -0.2205850857 4.160948e-04 -4.276913e-05 0.2199680869
genderM -0.0070029315 -9.131544e-04 1.718328e-03 0.0098809216
age -0.0068365860 -4.340308e-05 2.303236e-05 -0.0003318528
att0:att1 -0.0005167246 -9.600201e-04 1.466216e-04 -0.0028458192
educH genderM age att0:att1
(Intercept) -2.205851e-01 -0.0070029315 -6.836586e-03 -5.167246e-04
att0 4.160948e-04 -0.0009131544 -4.340308e-05 -9.600201e-04
att1 -4.276913e-05 0.0017183281 2.303236e-05 1.466216e-04
educM 2.199681e-01 0.0098809216 -3.318528e-04 -2.845819e-03
educH 2.530135e-01 0.0093081833 -7.717637e-05 -1.974714e-03
genderM 9.308183e-03 0.0580447377 -6.034870e-04 -3.374912e-04
age -7.717637e-05 -0.0006034870 1.701166e-04 3.548442e-05
att0:att1 -1.974714e-03 -0.0003374912 3.548442e-05 1.848475e-03
>
> ## extract regression weights
> w <- weights(neMod)
> head(w)
1 2 3 4 5 6
0.4119360 0.6517524 0.7949334 0.9259571 1.0826446 1.3394706
>
> ## obtain bootstrap confidence intervals
> confint(neMod)
95% LCL 95% UCL
(Intercept) -1.926667764 0.87490529
att0 0.058413178 0.52802447
att1 0.080036305 0.26927628
educM -0.775970263 1.16988497
educH -0.589005688 1.38273544
genderM -0.141594926 0.80281276
age -0.038274099 0.01285303
att0:att1 -0.004342831 0.16419014
> confint(neMod, parm = c("att0"))
95% LCL 95% UCL
att0 0.05841318 0.5280245
> confint(neMod, type = "perc", level = 0.90)
90% LCL 90% UCL
(Intercept) -1.701458349 0.649695875
att0 0.096163714 0.490273934
att1 0.095248696 0.254063891
educM -0.619549241 1.013463952
educH -0.430503783 1.224233532
genderM -0.065677041 0.726894875
age -0.034164154 0.008743082
att0:att1 0.009204991 0.150642321
>
> ## summary table
> summary(neMod)
Natural effect model
with robust standard errors based on the sandwich estimator
---
Exposure: att
Mediator(s): negaff
---
Parameter estimates:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.52588 0.71470 -0.736 0.461848
att0 0.29322 0.11980 2.448 0.014383 *
att1 0.17466 0.04828 3.618 0.000297 ***
educM 0.19696 0.49640 0.397 0.691536
educH 0.39686 0.50300 0.789 0.430119
genderM 0.33061 0.24092 1.372 0.169986
age -0.01271 0.01304 -0.975 0.329799
att0:att1 0.07992 0.04299 1.859 0.063034 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
> ## residual plots
> library(car)
Loading required package: carData
> residualPlots(neMod)
Warning in eval(family$initialize) :
non-integer #successes in a binomial glm!
Error in eval(extras, data, env) : object 'object' not found
Calls: residualPlots ... <Anonymous> -> model.frame.default -> eval -> eval -> weights
Execution halted
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-patched-linux-x86_64
Version: 0.6-7
Check: examples
Result: ERROR
Running examples in ‘medflex-Ex.R’ failed
The error most likely occurred in:
> ### Name: neModel-methods
> ### Title: Methods for natural effect models
> ### Aliases: neModel-methods coef.neModel confint.neModelBoot
> ### confint.neModel residualPlot.neModel residualPlots.neModel
> ### summary.neModel vcov.neModel weights.neModel
>
> ### ** Examples
>
> data(UPBdata)
>
> weightData <- neWeight(negaff ~ att + educ + gender + age,
+ data = UPBdata)
> neMod <- neModel(UPB ~ att0 * att1 + educ + gender + age,
+ family = binomial, expData = weightData, se = "robust")
>
> ## extract coefficients
> coef(neMod)
(Intercept) att0 att1 educM educH genderM
-0.52588124 0.29321882 0.17465629 0.19695736 0.39686487 0.33060892
age att0:att1
-0.01271054 0.07992366
>
> ## extract variance-covariance matrix
> vcov(neMod)
(Intercept) att0 att1 educM
(Intercept) 0.5107962853 1.205653e-04 -9.948131e-04 -0.2101703627
att0 0.0001205653 1.435228e-02 -1.692869e-03 0.0025776663
att1 -0.0009948131 -1.692869e-03 2.330610e-03 -0.0011022216
educM -0.2101703627 2.577666e-03 -1.102222e-03 0.2464137181
educH -0.2205850857 4.160948e-04 -4.276913e-05 0.2199680869
genderM -0.0070029315 -9.131544e-04 1.718328e-03 0.0098809216
age -0.0068365860 -4.340308e-05 2.303236e-05 -0.0003318528
att0:att1 -0.0005167246 -9.600201e-04 1.466216e-04 -0.0028458192
educH genderM age att0:att1
(Intercept) -2.205851e-01 -0.0070029315 -6.836586e-03 -5.167246e-04
att0 4.160948e-04 -0.0009131544 -4.340308e-05 -9.600201e-04
att1 -4.276913e-05 0.0017183281 2.303236e-05 1.466216e-04
educM 2.199681e-01 0.0098809216 -3.318528e-04 -2.845819e-03
educH 2.530135e-01 0.0093081833 -7.717637e-05 -1.974714e-03
genderM 9.308183e-03 0.0580447377 -6.034870e-04 -3.374912e-04
age -7.717637e-05 -0.0006034870 1.701166e-04 3.548442e-05
att0:att1 -1.974714e-03 -0.0003374912 3.548442e-05 1.848475e-03
>
> ## extract regression weights
> w <- weights(neMod)
> head(w)
1 2 3 4 5 6
0.4119360 0.6517524 0.7949334 0.9259571 1.0826446 1.3394706
>
> ## obtain bootstrap confidence intervals
> confint(neMod)
95% LCL 95% UCL
(Intercept) -1.926667764 0.87490529
att0 0.058413178 0.52802447
att1 0.080036305 0.26927628
educM -0.775970263 1.16988497
educH -0.589005688 1.38273544
genderM -0.141594926 0.80281276
age -0.038274099 0.01285303
att0:att1 -0.004342831 0.16419014
> confint(neMod, parm = c("att0"))
95% LCL 95% UCL
att0 0.05841318 0.5280245
> confint(neMod, type = "perc", level = 0.90)
90% LCL 90% UCL
(Intercept) -1.701458349 0.649695875
att0 0.096163714 0.490273934
att1 0.095248696 0.254063891
educM -0.619549241 1.013463952
educH -0.430503783 1.224233532
genderM -0.065677041 0.726894875
age -0.034164154 0.008743082
att0:att1 0.009204991 0.150642321
>
> ## summary table
> summary(neMod)
Natural effect model
with robust standard errors based on the sandwich estimator
---
Exposure: att
Mediator(s): negaff
---
Parameter estimates:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.52588 0.71470 -0.736 0.461848
att0 0.29322 0.11980 2.448 0.014383 *
att1 0.17466 0.04828 3.618 0.000297 ***
educM 0.19696 0.49640 0.397 0.691536
educH 0.39686 0.50300 0.789 0.430119
genderM 0.33061 0.24092 1.372 0.169986
age -0.01271 0.01304 -0.975 0.329799
att0:att1 0.07992 0.04299 1.859 0.063034 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
> ## residual plots
> library(car)
Loading required package: carData
> residualPlots(neMod)
Warning in eval(family$initialize) :
non-integer #successes in a binomial glm!
Error in eval(extras, data, env) : object 'object' not found
Calls: residualPlots ... <Anonymous> -> model.frame.default -> eval -> eval -> weights
Execution halted
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-macos-arm64, r-devel-macos-x86_64, r-devel-windows-x86_64
Version: 0.6-7
Check: re-building of vignette outputs
Result: NOTE
Error(s) in re-building vignettes:
--- re-building ‘medflex.Rnw’ using Sweave
Loading required package: multcomp
Loading required package: mvtnorm
Loading required package: survival
Loading required package: TH.data
Loading required package: MASS
Attaching package: 'TH.data'
The following object is masked from 'package:MASS':
geyser
medflex 0.6-7: Flexible Mediation Analysis Using Natural Effect Models
Please report bugs here: github.com/jmpsteen/medflex/issues
attbin01
0.3959
attbin11
0.352
Loading required package: carData
att0
1.34
att0 + att0:att1
1.44
att1
1.20
att1 + att0:att1
1.29
att0 + att1 + att0:att1
1.73
Effect decomposition on the scale of the linear predictor
conditional on: gender, educ, age
with x* = 0, x = 1
Effect decomposition on exp(scale of the linear predictor)
conditional on: gender, educ, age
with x* = 0, x = 1
Effect decomposition on the scale of the linear predictor
conditional on: gender, educ, age
with x* = 0, x = 1
Effect decomposition on exp(scale of the linear predictor)
conditional on: gender, educ, age
with x* = 0, x = 1
Error: processing vignette 'medflex.Rnw' failed with diagnostics:
Running 'texi2dvi' on 'medflex.tex' failed.
LaTeX errors:
! LaTeX Error: File `nccmath.sty' not found.
Type X to quit or <RETURN> to proceed,
or enter new name. (Default extension: sty)
! Emergency stop.
<read *>
l.12 ^^M
! ==> Fatal error occurred, no output PDF file produced!
--- failed re-building 'medflex.Rnw'
--- re-building ‘sandwich.Rnw’ using Sweave
--- finished re-building ‘sandwich.Rnw’
SUMMARY: processing the following file failed:
‘medflex.Rnw’
Error: Vignette re-building failed.
Execution halted
Flavor: r-devel-macos-x86_64
Version: 0.6-7
Check: examples
Result: ERROR
Running examples in ‘medflex-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: neModel-methods
> ### Title: Methods for natural effect models
> ### Aliases: neModel-methods coef.neModel confint.neModelBoot
> ### confint.neModel residualPlot.neModel residualPlots.neModel
> ### summary.neModel vcov.neModel weights.neModel
>
> ### ** Examples
>
> data(UPBdata)
>
> weightData <- neWeight(negaff ~ att + educ + gender + age,
+ data = UPBdata)
> neMod <- neModel(UPB ~ att0 * att1 + educ + gender + age,
+ family = binomial, expData = weightData, se = "robust")
>
> ## extract coefficients
> coef(neMod)
(Intercept) att0 att1 educM educH genderM
-0.52588124 0.29321882 0.17465629 0.19695736 0.39686487 0.33060892
age att0:att1
-0.01271054 0.07992366
>
> ## extract variance-covariance matrix
> vcov(neMod)
(Intercept) att0 att1 educM
(Intercept) 0.5107962853 1.205653e-04 -9.948131e-04 -0.2101703627
att0 0.0001205653 1.435228e-02 -1.692869e-03 0.0025776663
att1 -0.0009948131 -1.692869e-03 2.330610e-03 -0.0011022216
educM -0.2101703627 2.577666e-03 -1.102222e-03 0.2464137181
educH -0.2205850857 4.160948e-04 -4.276913e-05 0.2199680869
genderM -0.0070029315 -9.131544e-04 1.718328e-03 0.0098809216
age -0.0068365860 -4.340308e-05 2.303236e-05 -0.0003318528
att0:att1 -0.0005167246 -9.600201e-04 1.466216e-04 -0.0028458192
educH genderM age att0:att1
(Intercept) -2.205851e-01 -0.0070029315 -6.836586e-03 -5.167246e-04
att0 4.160948e-04 -0.0009131544 -4.340308e-05 -9.600201e-04
att1 -4.276913e-05 0.0017183281 2.303236e-05 1.466216e-04
educM 2.199681e-01 0.0098809216 -3.318528e-04 -2.845819e-03
educH 2.530135e-01 0.0093081833 -7.717637e-05 -1.974714e-03
genderM 9.308183e-03 0.0580447377 -6.034870e-04 -3.374912e-04
age -7.717637e-05 -0.0006034870 1.701166e-04 3.548442e-05
att0:att1 -1.974714e-03 -0.0003374912 3.548442e-05 1.848475e-03
>
> ## extract regression weights
> w <- weights(neMod)
> head(w)
1 2 3 4 5 6
0.4119360 0.6517524 0.7949334 0.9259571 1.0826446 1.3394706
>
> ## obtain bootstrap confidence intervals
> confint(neMod)
95% LCL 95% UCL
(Intercept) -1.926667764 0.87490529
att0 0.058413178 0.52802447
att1 0.080036305 0.26927628
educM -0.775970263 1.16988497
educH -0.589005688 1.38273544
genderM -0.141594926 0.80281276
age -0.038274099 0.01285303
att0:att1 -0.004342831 0.16419014
> confint(neMod, parm = c("att0"))
95% LCL 95% UCL
att0 0.05841318 0.5280245
> confint(neMod, type = "perc", level = 0.90)
90% LCL 90% UCL
(Intercept) -1.701458349 0.649695875
att0 0.096163714 0.490273934
att1 0.095248696 0.254063891
educM -0.619549241 1.013463952
educH -0.430503783 1.224233532
genderM -0.065677041 0.726894875
age -0.034164154 0.008743082
att0:att1 0.009204991 0.150642321
>
> ## summary table
> summary(neMod)
Natural effect model
with robust standard errors based on the sandwich estimator
---
Exposure: att
Mediator(s): negaff
---
Parameter estimates:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.52588 0.71470 -0.736 0.461848
att0 0.29322 0.11980 2.448 0.014383 *
att1 0.17466 0.04828 3.618 0.000297 ***
educM 0.19696 0.49640 0.397 0.691536
educH 0.39686 0.50300 0.789 0.430119
genderM 0.33061 0.24092 1.372 0.169986
age -0.01271 0.01304 -0.975 0.329799
att0:att1 0.07992 0.04299 1.859 0.063034 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
> ## residual plots
> library(car)
Loading required package: carData
> residualPlots(neMod)
Warning in eval(family$initialize) :
non-integer #successes in a binomial glm!
Error in weights(object$neModelFit, type = "prior") :
object 'object' not found
Calls: residualPlots ... <Anonymous> -> model.frame.default -> eval -> eval -> weights
Execution halted
Flavor: r-release-linux-x86_64
Version: 0.6-7
Check: examples
Result: ERROR
Running examples in 'medflex-Ex.R' failed
The error most likely occurred in:
> ### Name: neModel-methods
> ### Title: Methods for natural effect models
> ### Aliases: neModel-methods coef.neModel confint.neModelBoot
> ### confint.neModel residualPlot.neModel residualPlots.neModel
> ### summary.neModel vcov.neModel weights.neModel
>
> ### ** Examples
>
> data(UPBdata)
>
> weightData <- neWeight(negaff ~ att + educ + gender + age,
+ data = UPBdata)
> neMod <- neModel(UPB ~ att0 * att1 + educ + gender + age,
+ family = binomial, expData = weightData, se = "robust")
>
> ## extract coefficients
> coef(neMod)
(Intercept) att0 att1 educM educH genderM
-0.52588124 0.29321882 0.17465629 0.19695736 0.39686487 0.33060892
age att0:att1
-0.01271054 0.07992366
>
> ## extract variance-covariance matrix
> vcov(neMod)
(Intercept) att0 att1 educM
(Intercept) 0.5107962853 1.205653e-04 -9.948131e-04 -0.2101703627
att0 0.0001205653 1.435228e-02 -1.692869e-03 0.0025776663
att1 -0.0009948131 -1.692869e-03 2.330610e-03 -0.0011022216
educM -0.2101703627 2.577666e-03 -1.102222e-03 0.2464137181
educH -0.2205850857 4.160948e-04 -4.276913e-05 0.2199680869
genderM -0.0070029315 -9.131544e-04 1.718328e-03 0.0098809216
age -0.0068365860 -4.340308e-05 2.303236e-05 -0.0003318528
att0:att1 -0.0005167246 -9.600201e-04 1.466216e-04 -0.0028458192
educH genderM age att0:att1
(Intercept) -2.205851e-01 -0.0070029315 -6.836586e-03 -5.167246e-04
att0 4.160948e-04 -0.0009131544 -4.340308e-05 -9.600201e-04
att1 -4.276913e-05 0.0017183281 2.303236e-05 1.466216e-04
educM 2.199681e-01 0.0098809216 -3.318528e-04 -2.845819e-03
educH 2.530135e-01 0.0093081833 -7.717637e-05 -1.974714e-03
genderM 9.308183e-03 0.0580447377 -6.034870e-04 -3.374912e-04
age -7.717637e-05 -0.0006034870 1.701166e-04 3.548442e-05
att0:att1 -1.974714e-03 -0.0003374912 3.548442e-05 1.848475e-03
>
> ## extract regression weights
> w <- weights(neMod)
> head(w)
1 2 3 4 5 6
0.4119360 0.6517524 0.7949334 0.9259571 1.0826446 1.3394706
>
> ## obtain bootstrap confidence intervals
> confint(neMod)
95% LCL 95% UCL
(Intercept) -1.926667764 0.87490529
att0 0.058413178 0.52802447
att1 0.080036305 0.26927628
educM -0.775970263 1.16988497
educH -0.589005688 1.38273544
genderM -0.141594926 0.80281276
age -0.038274099 0.01285303
att0:att1 -0.004342831 0.16419014
> confint(neMod, parm = c("att0"))
95% LCL 95% UCL
att0 0.05841318 0.5280245
> confint(neMod, type = "perc", level = 0.90)
90% LCL 90% UCL
(Intercept) -1.701458349 0.649695875
att0 0.096163714 0.490273934
att1 0.095248696 0.254063891
educM -0.619549241 1.013463952
educH -0.430503783 1.224233532
genderM -0.065677041 0.726894875
age -0.034164154 0.008743082
att0:att1 0.009204991 0.150642321
>
> ## summary table
> summary(neMod)
Natural effect model
with robust standard errors based on the sandwich estimator
---
Exposure: att
Mediator(s): negaff
---
Parameter estimates:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.52588 0.71470 -0.736 0.461848
att0 0.29322 0.11980 2.448 0.014383 *
att1 0.17466 0.04828 3.618 0.000297 ***
educM 0.19696 0.49640 0.397 0.691536
educH 0.39686 0.50300 0.789 0.430119
genderM 0.33061 0.24092 1.372 0.169986
age -0.01271 0.01304 -0.975 0.329799
att0:att1 0.07992 0.04299 1.859 0.063034 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
>
> ## residual plots
> library(car)
Loading required package: carData
> residualPlots(neMod)
Warning in eval(family$initialize) :
non-integer #successes in a binomial glm!
Error in weights(object$neModelFit, type = "prior") :
object 'object' not found
Calls: residualPlots ... <Anonymous> -> model.frame.default -> eval -> eval -> weights
Execution halted
Flavors: r-release-windows-x86_64, r-oldrel-windows-ix86+x86_64