CRAN Package Check Results for Package tscount

Last updated on 2020-01-27 00:48:25 CET.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 1.4.1 5.61 166.84 172.45 ERROR
r-devel-linux-x86_64-debian-gcc 1.4.1 4.93 132.49 137.42 NOTE
r-devel-linux-x86_64-fedora-clang 1.4.1 220.03 OK
r-devel-linux-x86_64-fedora-gcc 1.4.1 229.50 OK
r-devel-windows-ix86+x86_64 1.4.1 16.00 286.00 302.00 OK
r-devel-windows-ix86+x86_64-gcc8 1.4.1 17.00 288.00 305.00 OK
r-patched-linux-x86_64 1.4.1 5.10 158.86 163.96 OK
r-patched-solaris-x86 1.4.1 229.60 OK
r-release-linux-x86_64 1.4.1 4.72 163.33 168.05 OK
r-release-windows-ix86+x86_64 1.4.1 8.00 167.00 175.00 OK
r-release-osx-x86_64 1.4.1 OK
r-oldrel-windows-ix86+x86_64 1.4.1 10.00 211.00 221.00 OK
r-oldrel-osx-x86_64 1.4.1 OK

Check Details

Version: 1.4.1
Check: tests
Result: ERROR
     Running 'test_code.R' [41s/48s]
    Running the tests in 'tests/test_code.R' failed.
    Complete output:
     > library(tscount)
     >
     > begin <- Sys.time()
     >
     > checkfit <- function(seed, n=50, model, param, xreg=NULL, distr="poisson", distrcoefs=NULL, link="identity", startestims=FALSE, extended=FALSE, interv=FALSE, ...){
     + if(missing(seed)) seed <- 1945
     + set.seed(seed)
     + #Simulate a time series from the given model:
     + timser <- tsglm.sim(n=n, param=param, model=model, xreg=xreg, link=link, distr=distr, distrcoefs=distrcoefs)$ts
     + print(unlist(param))
     + #Fit the given model to the time series:
     + print(fit <- tsglm(ts=timser, model=model, xreg=xreg, link=link, distr=distr, ...))
     + if(startestims){ #Try the different methods for start estimation:
     + tsglm(ts=timser, model=model, xreg=xreg, link=link, distr=distr, start.control=list(method="iid"), final.control=NULL)
     + tsglm(ts=timser, model=model, xreg=xreg, link=link, distr=distr, start.control=list(method="GLM"), final.control=NULL)
     + tsglm(ts=timser, model=model, xreg=xreg, link=link, distr=distr, start.control=list(method="CSS"), final.control=NULL)
     + tsglm(ts=timser, model=model, xreg=xreg, link=link, distr=distr, start.control=list(method="MM"), final.control=NULL)
     + }
     + if(extended){ #Apply the standard methods for the fitted model:
     + summary(fit)
     + residuals(fit, type="pearson")
     + residuals(fit, type="anscombe")
     + par(mfrow=c(3,2))
     + plot(fit, ask=FALSE)
     + fitted(fit)
     + coef(fit)
     + predict(fit, n.ahead=4)
     + #logLik(fit) #already included in summary-method
     + vcov(fit)
     + #AIC(fit) #already included in summary-method
     + #BIC(fit) #already included in summary-method
     + se(fit, B=3)
     + #pit(fit) #already included in plot-method
     + #marcal(fit) #already included in plot-method
     + scoring(fit)
     + }
     + if(interv){
     + interv_test(fit, tau=floor(n/2), delta=0.8, external=FALSE, est_interv=TRUE, ...)
     + interv_detect(fit, taus=floor(0.45*n):ceiling(0.55*n), delta=0.8, B=3, ...)
     + interv_multiple(fit, taus=floor(0.45*n):ceiling(0.55*n), deltas=c(0,1), B=3, final.control_bootstrap=NULL, ...)
     + }
     + return(TRUE)
     + }
     >
     > checkfit(model=list(past_obs=1, past_mean=1), param=list(intercept=2, past_obs=0.4, past_mean=0.3), startestims=TRUE, extended=TRUE)
     intercept past_obs past_mean
     2.0 0.4 0.3
    
     Call:
     tsglm(ts = timser, model = model, xreg = xreg, link = link, distr = distr)
    
     Coefficients:
     (Intercept) beta_1 alpha_1
     4.36248 0.47076 0.02301
    
     [1] TRUE
     > checkfit(model=list(past_obs=1, past_mean=1), param=list(intercept=2, past_obs=0.4, past_mean=0.3, xreg=c(4)), xreg=matrix(rexp(1*50, rate=3), ncol=1), startestims=TRUE, extended=TRUE) #one covariate
     intercept past_obs past_mean xreg
     2.0 0.4 0.3 4.0
    
     Call:
     tsglm(ts = timser, model = model, xreg = xreg, link = link, distr = distr)
    
     Coefficients:
     (Intercept) beta_1 alpha_1 eta_1
     2.66696 0.04534 0.48532 9.85239
    
     [1] TRUE
     > checkfit(model=list(past_obs=1, past_mean=1), param=list(intercept=2, past_obs=0.4, past_mean=0.3, xreg=c(4,2)), xreg=matrix(rexp(2*50, rate=3), ncol=2), startestims=TRUE, extended=TRUE) #two covariates
     intercept past_obs past_mean xreg1 xreg2
     2.0 0.4 0.3 4.0 2.0
    
     Call:
     tsglm(ts = timser, model = model, xreg = xreg, link = link, distr = distr)
    
     Coefficients:
     (Intercept) beta_1 alpha_1 eta_1 eta_2
     0.9103 0.3786 0.4741 0.9416 2.0897
    
     [1] TRUE
     > checkfit(model=list(past_obs=1), param=list(intercept=2, past_obs=0.4))
     intercept past_obs
     2.0 0.4
    
     Call:
     tsglm(ts = timser, model = model, xreg = xreg, link = link, distr = distr)
    
     Coefficients:
     (Intercept) beta_1
     2.6873 0.3656
    
     [1] TRUE
     > checkfit(model=list(), param=list(intercept=2))
     intercept
     2
    
     Call:
     tsglm(ts = timser, model = model, xreg = xreg, link = link, distr = distr)
    
     Coefficients:
     (Intercept)
     2.42
    
     [1] TRUE
     > checkfit(model=list(past_obs=1:2, past_mean=1:2), param=list(intercept=2, past_obs=c(0.3,0.2), past_mean=c(0.2,0.1)))
     intercept past_obs1 past_obs2 past_mean1 past_mean2
     2.0 0.3 0.2 0.2 0.1
    
     Call:
     tsglm(ts = timser, model = model, xreg = xreg, link = link, distr = distr)
    
     Coefficients:
     (Intercept) beta_1 beta_2 alpha_1 alpha_2
     1.097e+01 8.592e-12 1.299e-01 2.234e-03 2.014e-03
    
     [1] TRUE
     > checkfit(model=list(past_obs=1, past_mean=1), param=list(intercept=2, past_obs=0.4, past_mean=0.3), distr="nbinom", distrcoefs=c(size=2), extended=TRUE, start.control=list(use=20))
     intercept past_obs past_mean
     2.0 0.4 0.3
    
     Call:
     tsglm(ts = timser, model = model, xreg = xreg, link = link, distr = distr,
     start.control = ..1)
    
     Coefficients:
     (Intercept) beta_1 alpha_1
     2.8027 0.5440 0.1208
    
     Overdispersion coefficient 'sigmasq' was estimated to be 0.3211238.
    
     [1] TRUE
     > checkfit(model=list(past_obs=1, past_mean=1), param=list(intercept=2, past_obs=0.4, past_mean=0.3), distr="nbinom", distrcoefs=c(size=2), extended=TRUE, init.drop=TRUE)
     intercept past_obs past_mean
     2.0 0.4 0.3
    
     Call:
     tsglm(ts = timser, model = model, xreg = xreg, link = link, distr = distr,
     init.drop = TRUE)
    
     Coefficients:
     (Intercept) beta_1 alpha_1
     2.7022 0.5488 0.1313
    
     Overdispersion coefficient 'sigmasq' was estimated to be 0.3292575.
    
     [1] TRUE
     > checkfit(model=list(past_obs=1, past_mean=1), param=list(intercept=0.5, past_obs=0.4, past_mean=0.3), link="log", startestims=TRUE, extended=TRUE)
     intercept past_obs past_mean
     0.5 0.4 0.3
    
     Call:
     tsglm(ts = timser, model = model, xreg = xreg, link = link, distr = distr)
    
     Coefficients:
     (Intercept) beta_1 alpha_1
     2.4338 0.3250 -0.5352
    
     [1] TRUE
     > checkfit(model=list(past_obs=1, past_mean=1), param=list(intercept=0.5, past_obs=0.4, past_mean=0.3), link="log", distr="nbinom", distrcoefs=c(size=2), extended=TRUE, interv=TRUE)
     intercept past_obs past_mean
     0.5 0.4 0.3
    
     Call:
     tsglm(ts = timser, model = model, xreg = xreg, link = link, distr = distr)
    
     Coefficients:
     (Intercept) beta_1 alpha_1
     0.6058 0.4157 0.2108
    
     Overdispersion coefficient 'sigmasq' was estimated to be 0.4746927.
    
     ----------- FAILURE REPORT --------------
     --- failure: the condition has length > 1 ---
     --- srcref ---
     :
     --- package (from environment) ---
     tscount
     --- call from context ---
     invertinfo(G_11, ...)
     --- call from argument ---
     if (class(vcov) == "try-error") {
     result$error_message <- paste("Error in invertinfo(mat) : \n",
     vcov[[1]], sep = "")
     result$vcov <- matrix(NA, nrow = nrow(mat), ncol = ncol(mat))
     } else {
     result$vcov <- vcov
     }
     --- R stacktrace ---
     where 1: invertinfo(G_11, ...)
     where 2: scoretest(Score = loglik$score, G = loglik$info, G1 = infomat_corrected,
     r = 1, stopOnError = stopOnError, silent = TRUE)
     where 3: (function (model, ts, xreg, link, distr, fit_H0 = NULL, taus,
     delta, external, est_interv = FALSE, stopOnError = FALSE,
     ...)
     {
     n <- length(ts)
     if (all(ts == 0))
     return(list(error_message = "Time series is constantly zero"))
     if (is.null(fit_H0)) {
     op <- options(show.error.messages = FALSE)
     fit_H0 <- try(tsglm(ts = ts, model = model, xreg = xreg,
     link = link, distr = distr, score = FALSE, info = "none",
     ...))
     options(op)
     if ("try-error" %in% class(fit_H0))
     return(list(error_message = fit_H0[[1]]))
     }
     param_H0_extended <- c(fit_H0$coefficients, 0)
     model_extended <- model
     xreg_extended <- xreg
     xreg_extended <- cbind(xreg, numeric(n))
     model_extended$external <- c(model$external, external)
     condmean_H0 <- tsglm.condmean(link = link, paramvec = param_H0_extended,
     model = model_extended, ts = ts, xreg = xreg_extended,
     derivatives = ifelse(info == "hessian", "second", "first"))
     test_statistic_tau <- as.numeric(rep(NA, length(taus)))
     names(test_statistic_tau) <- taus
     for (j in seq(along = taus)) {
     xreg_extended <- cbind(xreg, interv_covariate(n = n,
     tau = taus[j], delta = delta))
     loglik <- tsglm.loglik(link = link, paramvec = param_H0_extended,
     model = model_extended, ts = ts, xreg = xreg_extended,
     score = TRUE, info = info, condmean = condmean_H0,
     from = taus[j])
     infomat_corrected <- apply((1/loglik$nu + fit$sigmasq) *
     loglik$outerscoreprod, c(2, 3), sum)
     test_statistic_temp <- scoretest(Score = loglik$score,
     G = loglik$info, G1 = infomat_corrected, r = 1, stopOnError = stopOnError,
     silent = TRUE)
     if (!is.null(test_statistic_temp$error_message)) {
     return(list(error_message = test_statistic_temp$error_message))
     }
     test_statistic_tau[j] <- test_statistic_temp$test_statistic
     }
     index_tau_max <- which.max(test_statistic_tau)
     tau_max <- taus[index_tau_max]
     test_statistic <- test_statistic_tau[index_tau_max]
     covariate <- interv_covariate(n = n, tau = tau_max, delta = delta)
     xreg_extended <- cbind(xreg, covariate)
     colnames(xreg_extended) <- c(colnames(xreg), colnames(covariate))
     result <- list(test_statistic = test_statistic, test_statistic_tau = test_statistic_tau,
     tau_max = tau_max, fit_H0 = fit_H0)
     if (est_interv) {
     fit_interv <- try(tsglm(ts = ts, model = model_extended,
     xreg = xreg_extended, link = link, distr = distr,
     score = FALSE, info = "none", ...))
     result <- c(result, list(fit_interv = fit_interv))
     }
     result <- c(result, list(model_interv = model_extended, xreg_interv = xreg_extended))
     return(result)
     })(model = list(past_obs = 1, past_mean = 1, external = logical(0)),
     ts = c(5, 1, 0, 3, 3, 0, 3, 17, 5, 11, 5, 2, 7, 3, 5, 3,
     6, 1, 0, 1, 2, 0, 1, 1, 2, 4, 3, 3, 1, 2, 2, 6, 7, 1, 6,
     10, 3, 11, 6, 18, 8, 5, 1, 10, 3, 12, 14, 7, 6, 1), xreg = numeric(0),
     link = "log", distr = "nbinom", fit_H0 = NULL, taus = 22:28,
     delta = 0.8, external = FALSE, est_interv = TRUE, start.control = list(),
     final.control = list(), inter.control = list())
     where 4: do.call(compute_test_statistic, args = c(list(model = model,
     ts = ts.bootstrap, xreg = xreg, link = link, distr = distr,
     fit_H0 = fit_H0.bootstrap, taus = taus, delta = delta, external = external,
     est_interv = TRUE, start.control = start.control_bootstrap,
     final.control = final.control_bootstrap, inter.control = inter.control_bootstrap),
     dotdotdot))
     where 5: FUN(X[[i]], ...)
     where 6: lapply(X = X, FUN = FUN, ...)
     where 7: sapply(X = X, FUN = FUN, ..., simplify = FALSE)
     where 8: Sapply(seeds, bootstrap, fit_H0 = fit, n = fit$n_obs, model = fit$model,
     xreg = fit$xreg, link = fit$link, distr = fit$distr, taus = taus,
     delta = delta, external = external, ...)
     where 9: interv_detect.tsglm(fit, taus = floor(0.45 * n):ceiling(0.55 *
     n), delta = 0.8, B = 3, ...)
     where 10: interv_detect(fit, taus = floor(0.45 * n):ceiling(0.55 * n),
     delta = 0.8, B = 3, ...)
     where 11: checkfit(model = list(past_obs = 1, past_mean = 1), param = list(intercept = 0.5,
     past_obs = 0.4, past_mean = 0.3), link = "log", distr = "nbinom",
     distrcoefs = c(size = 2), extended = TRUE, interv = TRUE)
    
     --- value of length: 2 type: logical ---
     [1] FALSE FALSE
     --- function from context ---
     function (mat, silent = TRUE, stopOnError = FALSE)
     {
     if (stopOnError) {
     result <- list(vcov = chol2inv(chol(mat)), error_message = NULL)
     }
     else {
     result <- list(vcov = matrix(NA, ncol = ncol(mat)), error_message = NULL)
     vcov <- try(chol2inv(chol(mat)), silent = silent)
     if (class(vcov) == "try-error") {
     result$error_message <- paste("Error in invertinfo(mat) : \n",
     vcov[[1]], sep = "")
     result$vcov <- matrix(NA, nrow = nrow(mat), ncol = ncol(mat))
     }
     else {
     result$vcov <- vcov
     }
     }
     dimnames(result$vcov) <- dimnames(mat)
     return(result)
     }
     <bytecode: 0x47614f0>
     <environment: namespace:tscount>
     --- function search by body ---
     Function invertinfo in namespace tscount has this body.
     ----------- END OF FAILURE REPORT --------------
     Error in if (class(vcov) == "try-error") { : the condition has length > 1
     Calls: checkfit ... FUN -> do.call -> <Anonymous> -> scoretest -> invertinfo
     Execution halted
Flavor: r-devel-linux-x86_64-debian-clang

Version: 1.4.1
Check: package dependencies
Result: NOTE
    Package suggested but not available for checking: ‘surveillance’
Flavor: r-devel-linux-x86_64-debian-gcc