CRAN Package Check Results for Package gsarima

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

Check Details

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