Last updated on 2020-09-03 11:59:30 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 0.1-4 | 3.04 | 26.03 | 29.07 | ERROR | |
| r-devel-linux-x86_64-debian-gcc | 0.1-4 | 1.93 | 20.91 | 22.84 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 0.1-4 | 42.48 | ERROR | |||
| r-devel-linux-x86_64-fedora-gcc | 0.1-4 | 32.75 | ERROR | |||
| r-devel-windows-ix86+x86_64 | 0.1-4 | 6.00 | 33.00 | 39.00 | ERROR | |
| r-patched-linux-x86_64 | 0.1-4 | 2.69 | 26.51 | 29.20 | NOTE | |
| r-patched-solaris-x86 | 0.1-4 | 47.50 | ERROR | |||
| r-release-linux-x86_64 | 0.1-4 | 2.73 | 26.65 | 29.38 | NOTE | |
| r-release-macos-x86_64 | 0.1-4 | NOTE | ||||
| r-release-windows-ix86+x86_64 | 0.1-4 | 7.00 | 37.00 | 44.00 | NOTE | |
| r-oldrel-macos-x86_64 | 0.1-4 | NOTE | ||||
| r-oldrel-windows-ix86+x86_64 | 0.1-4 | 4.00 | 47.00 | 51.00 | NOTE |
Version: 0.1-4
Check: R code for possible problems
Result: NOTE
garsim: no visible global function definition for 'rnorm'
garsim: no visible global function definition for 'rpois'
Undefined global functions or variables:
rnorm rpois
Consider adding
importFrom("stats", "rnorm", "rpois")
to your NAMESPACE file.
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-windows-ix86+x86_64, r-patched-linux-x86_64, r-patched-solaris-x86, r-release-linux-x86_64, r-release-macos-x86_64, r-release-windows-ix86+x86_64, r-oldrel-macos-x86_64, r-oldrel-windows-ix86+x86_64
Version: 0.1-4
Check: examples
Result: ERROR
Running examples in 'gsarima-Ex.R' failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: garsim
> ### Title: Simulate a Generalized Autoregressive Time Series
> ### Aliases: garsim
> ### Keywords: ts
>
> ### ** Examples
>
> N<-1000
> ar<-c(0.8)
> intercept<-2
> frequency<-1
> x<- rnorm(N)
> beta.x<-0.7
> #Gaussian simulation with covariate
> X=matrix(c(rep(intercept, N+length(ar)), rep(0, length(ar)), x), ncol=2)
> y.sim <- garsim(n=(N+length(ar)),phi=ar, X=X, beta=c(1,beta.x), sd=sqrt(1))
> y<-y.sim[(1+length(ar)):(N+length(ar))]
> tsy<-ts(y, freq=frequency)
> plot(tsy)
> arima(tsy, order=c(1,0,0), xreg=x)
Call:
arima(x = tsy, order = c(1, 0, 0), xreg = x)
Coefficients:
ar1 intercept x
0.8282 1.9235 0.6702
s.e. 0.0177 0.1900 0.0240
sigma^2 estimated as 1.076: log likelihood = -1456.02, aic = 2920.03
>
> #Gaussian simulation with covariate and deterministic seasonality through first order harmonic
> ar<-c(1.4,-0.4)
> frequency<-12
> beta.x<-c(0.7,4,4)
> X<-matrix(nrow= (N+ length(ar)), ncol=3)
> for (t in 1: length(ar)){
+ X[t,1]<-0
+ X[t,2]<-sin(2*pi*(t- length(ar))/frequency)
+ X[t,3]<- cos(2*pi*(t- length(ar))/frequency)
+ }
> for (t in (1+ length(ar)): (N+ length(ar))){
+ X[t,1]<-x[t- length(ar)]
+ X[t,2]<-sin(2*pi*(t- length(ar))/frequency)
+ X[t,3]<- cos(2*pi*(t- length(ar))/frequency)
+ }
> y.sim <- garsim(n=(N+length(ar)),phi=ar, X=X, beta= beta.x, sd=sqrt(1))
> y<-y.sim[(1+length(ar)):(N+length(ar))]
> tsy<-ts(y, freq=frequency)
> plot(tsy)
> Xreg<-matrix(nrow= N, ncol=3)
> for (t in 1: N){
+ Xreg[t,1]<-x[t]
+ Xreg[t,2]<-sin(2*pi*t/frequency)
+ Xreg[t,3]<- cos(2*pi*t/frequency)
+ }
> arimares<-arima(tsy, order=c(1,1,0), xreg=Xreg)
> tsdiag(arimares)
> arimares
Call:
arima(x = tsy, order = c(1, 1, 0), xreg = Xreg)
Coefficients:
ar1 Xreg1 Xreg2 Xreg3
0.4289 0.6744 4.0789 4.0440
s.e. 0.0286 0.0171 0.1337 0.1335
sigma^2 estimated as 1.055: log likelihood = -1444.3, aic = 2898.6
>
> #Negative binomial simulation with covariate
> ar<-c(0.8)
> frequency<-1
> beta.x<-0.7
> X=matrix(c(rep(log(intercept), N+length(ar)), rep(0, length(ar)), x), ncol=2)
> y.sim <- garsim(n=(N+length(ar)), phi=ar, beta=c(1,beta.x), link= "log",
+ family= "negative.binomial", zero.correction = "zq1", c=1, theta=5, X=X)
> y<-y.sim[(1+length(ar)):(N+length(ar))]
> tsy<-ts(y, freq=frequency)
> plot(tsy)
> library(gamlss.util)
Loading required package: gamlss.dist
Loading required package: MASS
Loading required package: gamlss
Loading required package: splines
Loading required package: gamlss.data
Attaching package: 'gamlss.data'
The following object is masked from 'package:datasets':
sleep
Loading required package: nlme
Loading required package: parallel
********** GAMLSS Version 5.1-7 **********
For more on GAMLSS look at http://www.gamlss.com/
Type gamlssNews() to see new features/changes/bug fixes.
Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, as.Date.numeric
> m10<-garmaFit(y~x-1, order=c(1,0), family=NBI, alpha=1)
Error in model.frame.default(formula = y ~ x - 1, data = data) :
'data' must be a data.frame, environment, or list
Calls: garmaFit ... gamlss -> eval -> eval -> model.frame -> model.frame.default
Execution halted
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc
Version: 0.1-4
Check: package dependencies
Result: NOTE
Package suggested but not available for checking: ‘gamlss.util’
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-patched-solaris-x86
Version: 0.1-4
Check: examples
Result: ERROR
Running examples in ‘gsarima-Ex.R’ failed
The error most likely occurred in:
> ### Name: garsim
> ### Title: Simulate a Generalized Autoregressive Time Series
> ### Aliases: garsim
> ### Keywords: ts
>
> ### ** Examples
>
> N<-1000
> ar<-c(0.8)
> intercept<-2
> frequency<-1
> x<- rnorm(N)
> beta.x<-0.7
> #Gaussian simulation with covariate
> X=matrix(c(rep(intercept, N+length(ar)), rep(0, length(ar)), x), ncol=2)
> y.sim <- garsim(n=(N+length(ar)),phi=ar, X=X, beta=c(1,beta.x), sd=sqrt(1))
> y<-y.sim[(1+length(ar)):(N+length(ar))]
> tsy<-ts(y, freq=frequency)
> plot(tsy)
> arima(tsy, order=c(1,0,0), xreg=x)
Call:
arima(x = tsy, order = c(1, 0, 0), xreg = x)
Coefficients:
ar1 intercept x
0.8282 1.9235 0.6702
s.e. 0.0177 0.1900 0.0240
sigma^2 estimated as 1.076: log likelihood = -1456.02, aic = 2920.03
>
> #Gaussian simulation with covariate and deterministic seasonality through first order harmonic
> ar<-c(1.4,-0.4)
> frequency<-12
> beta.x<-c(0.7,4,4)
> X<-matrix(nrow= (N+ length(ar)), ncol=3)
> for (t in 1: length(ar)){
+ X[t,1]<-0
+ X[t,2]<-sin(2*pi*(t- length(ar))/frequency)
+ X[t,3]<- cos(2*pi*(t- length(ar))/frequency)
+ }
> for (t in (1+ length(ar)): (N+ length(ar))){
+ X[t,1]<-x[t- length(ar)]
+ X[t,2]<-sin(2*pi*(t- length(ar))/frequency)
+ X[t,3]<- cos(2*pi*(t- length(ar))/frequency)
+ }
> y.sim <- garsim(n=(N+length(ar)),phi=ar, X=X, beta= beta.x, sd=sqrt(1))
> y<-y.sim[(1+length(ar)):(N+length(ar))]
> tsy<-ts(y, freq=frequency)
> plot(tsy)
> Xreg<-matrix(nrow= N, ncol=3)
> for (t in 1: N){
+ Xreg[t,1]<-x[t]
+ Xreg[t,2]<-sin(2*pi*t/frequency)
+ Xreg[t,3]<- cos(2*pi*t/frequency)
+ }
> arimares<-arima(tsy, order=c(1,1,0), xreg=Xreg)
> tsdiag(arimares)
> arimares
Call:
arima(x = tsy, order = c(1, 1, 0), xreg = Xreg)
Coefficients:
ar1 Xreg1 Xreg2 Xreg3
0.4289 0.6744 4.0789 4.0440
s.e. 0.0286 0.0171 0.1337 0.1335
sigma^2 estimated as 1.055: log likelihood = -1444.3, aic = 2898.6
>
> #Negative binomial simulation with covariate
> ar<-c(0.8)
> frequency<-1
> beta.x<-0.7
> X=matrix(c(rep(log(intercept), N+length(ar)), rep(0, length(ar)), x), ncol=2)
> y.sim <- garsim(n=(N+length(ar)), phi=ar, beta=c(1,beta.x), link= "log",
+ family= "negative.binomial", zero.correction = "zq1", c=1, theta=5, X=X)
> y<-y.sim[(1+length(ar)):(N+length(ar))]
> tsy<-ts(y, freq=frequency)
> plot(tsy)
> library(gamlss.util)
Error in library(gamlss.util) : there is no package called ‘gamlss.util’
Execution halted
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-patched-solaris-x86
Version: 0.1-4
Check: package dependencies
Result: NOTE
Package suggested but not available for checking: 'dse1'
Flavors: r-devel-windows-ix86+x86_64, r-release-windows-ix86+x86_64, r-oldrel-windows-ix86+x86_64
Version: 0.1-4
Check: examples
Result: ERROR
Running examples in 'gsarima-Ex.R' failed
The error most likely occurred in:
> ### Name: garsim
> ### Title: Simulate a Generalized Autoregressive Time Series
> ### Aliases: garsim
> ### Keywords: ts
>
> ### ** Examples
>
> N<-1000
> ar<-c(0.8)
> intercept<-2
> frequency<-1
> x<- rnorm(N)
> beta.x<-0.7
> #Gaussian simulation with covariate
> X=matrix(c(rep(intercept, N+length(ar)), rep(0, length(ar)), x), ncol=2)
> y.sim <- garsim(n=(N+length(ar)),phi=ar, X=X, beta=c(1,beta.x), sd=sqrt(1))
> y<-y.sim[(1+length(ar)):(N+length(ar))]
> tsy<-ts(y, freq=frequency)
> plot(tsy)
> arima(tsy, order=c(1,0,0), xreg=x)
Call:
arima(x = tsy, order = c(1, 0, 0), xreg = x)
Coefficients:
ar1 intercept x
0.8282 1.9235 0.6702
s.e. 0.0177 0.1900 0.0240
sigma^2 estimated as 1.076: log likelihood = -1456.02, aic = 2920.03
>
> #Gaussian simulation with covariate and deterministic seasonality through first order harmonic
> ar<-c(1.4,-0.4)
> frequency<-12
> beta.x<-c(0.7,4,4)
> X<-matrix(nrow= (N+ length(ar)), ncol=3)
> for (t in 1: length(ar)){
+ X[t,1]<-0
+ X[t,2]<-sin(2*pi*(t- length(ar))/frequency)
+ X[t,3]<- cos(2*pi*(t- length(ar))/frequency)
+ }
> for (t in (1+ length(ar)): (N+ length(ar))){
+ X[t,1]<-x[t- length(ar)]
+ X[t,2]<-sin(2*pi*(t- length(ar))/frequency)
+ X[t,3]<- cos(2*pi*(t- length(ar))/frequency)
+ }
> y.sim <- garsim(n=(N+length(ar)),phi=ar, X=X, beta= beta.x, sd=sqrt(1))
> y<-y.sim[(1+length(ar)):(N+length(ar))]
> tsy<-ts(y, freq=frequency)
> plot(tsy)
> Xreg<-matrix(nrow= N, ncol=3)
> for (t in 1: N){
+ Xreg[t,1]<-x[t]
+ Xreg[t,2]<-sin(2*pi*t/frequency)
+ Xreg[t,3]<- cos(2*pi*t/frequency)
+ }
> arimares<-arima(tsy, order=c(1,1,0), xreg=Xreg)
> tsdiag(arimares)
> arimares
Call:
arima(x = tsy, order = c(1, 1, 0), xreg = Xreg)
Coefficients:
ar1 Xreg1 Xreg2 Xreg3
0.4289 0.6744 4.0789 4.0440
s.e. 0.0286 0.0171 0.1337 0.1335
sigma^2 estimated as 1.055: log likelihood = -1444.3, aic = 2898.6
>
> #Negative binomial simulation with covariate
> ar<-c(0.8)
> frequency<-1
> beta.x<-0.7
> X=matrix(c(rep(log(intercept), N+length(ar)), rep(0, length(ar)), x), ncol=2)
> y.sim <- garsim(n=(N+length(ar)), phi=ar, beta=c(1,beta.x), link= "log",
+ family= "negative.binomial", zero.correction = "zq1", c=1, theta=5, X=X)
> y<-y.sim[(1+length(ar)):(N+length(ar))]
> tsy<-ts(y, freq=frequency)
> plot(tsy)
> library(gamlss.util)
Loading required package: gamlss.dist
Loading required package: MASS
Loading required package: gamlss
Loading required package: splines
Loading required package: gamlss.data
Attaching package: 'gamlss.data'
The following object is masked from 'package:datasets':
sleep
Loading required package: nlme
Loading required package: parallel
********** GAMLSS Version 5.1-7 **********
For more on GAMLSS look at http://www.gamlss.com/
Type gamlssNews() to see new features/changes/bug fixes.
Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, as.Date.numeric
> m10<-garmaFit(y~x-1, order=c(1,0), family=NBI, alpha=1)
Error in model.frame.default(formula = y ~ x - 1, data = data) :
'data' must be a data.frame, environment, or list
Calls: garmaFit ... gamlss -> eval -> eval -> model.frame -> model.frame.default
Execution halted
Flavor: r-devel-windows-ix86+x86_64