Check results for 'glmmrBase'

using R version 4.5.2 Patched (2026-01-31 r89382)
using platform: x86_64-apple-darwin20
R was compiled by Apple clang version 14.0.0 (clang-1400.0.29.202) GNU Fortran (GCC) 14.2.0
running under: macOS Ventura 13.3.1
using session charset: UTF-8
checking for file ‘glmmrBase/DESCRIPTION’ ... OK
checking extension type ... Package
this is package ‘glmmrBase’ version ‘1.4.0’
package encoding: UTF-8
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checking whether package ‘glmmrBase’ can be installed ... [257s/356s] OK See the install log for details.
used C++ compiler: ‘Apple clang version 14.0.3 (clang-1403.0.22.14.1)’
used SDK: ‘MacOSX11.3.1.sdk’
checking C++ specification ... OK
checking installed package size ... INFO installed size is 155.3Mb sub-directories of 1Mb or more: libs 154.1Mb
checking package directory ... OK
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checking examples ... [2s/5s] ERROR Running examples in ‘glmmrBase-Ex.R’ failed The error most likely occurred in: > ### Name: Model > ### Title: A GLMM Model > ### Aliases: Model > > ### ** Examples > > > ## ------------------------------------------------ > ## Method `Model$new` > ## ------------------------------------------------ > > ## Don't show: > setParallel(FALSE) > ## End(Don't show) > # For more examples, see the examples for MCML. > > #create a data frame describing a cross-sectional parallel cluster > #randomised trial > df <- nelder(~(cl(10)*t(5)) > ind(10)) > df$int <- 0 > df[df$cl > 5, 'int'] <- 1 > mod <- Model$new( + formula = ~ factor(t) + int - 1 + (1|gr(cl)) + (1|gr(cl,t)), + data = df, + family = stats::gaussian() + ) > > # We can also include the outcome data in the model initialisation. > # For example, simulating data and creating a new object: > df$y <- mod$sim_data() > > mod <- Model$new( + formula = y ~ factor(t) + int - 1 + (1|gr(cl)) + (1|gr(cl,t)), + data = df, + family = stats::gaussian() + ) > > # Here we will specify a cohort study > df <- nelder(~ind(20) * t(6)) > df$int <- 0 > df[df$t > 3, 'int'] <- 1 > > des <- Model$new( + formula = ~ int + (1|gr(ind)), + data = df, + family = stats::poisson() + ) > > # or with parameter values specified > > des <- Model$new( + formula = ~ int + (1|gr(ind)), + covariance = c(0.05), + mean = c(1,0.5), + data = df, + family = stats::poisson() + ) > > #an example of a spatial grid with two time points > > df <- nelder(~ (x(10)*y(10))*t(2)) > spt_design <- Model$new(formula = ~ 1 + (1|ar0(t)*fexp(x,y)), + data = df, + family = stats::gaussian()) > > ## ------------------------------------------------ > ## Method `Model$sim_data` > ## ------------------------------------------------ > > df <- nelder(~(cl(10)*t(5)) > ind(10)) > df$int <- 0 > df[df$cl > 5, 'int'] <- 1 > ## Don't show: > setParallel(FALSE) # for the CRAN check > ## End(Don't show) > des <- Model$new( + formula = ~ factor(t) + int - 1 + (1|gr(cl)*ar0(t)), + covariance = c(0.05,0.8), + mean = c(rep(0,5),0.6), + data = df, + family = stats::binomial() + ) > ysim <- des$sim_data() > > ## ------------------------------------------------ > ## Method `Model$update_parameters` > ## ------------------------------------------------ > > ## Don't show: > setParallel(FALSE) # for the CRAN check > ## End(Don't show) > df <- nelder(~(cl(10)*t(5)) > ind(10)) > df$int <- 0 > df[df$cl > 5, 'int'] <- 1 > des <- Model$new( + formula = ~ factor(t) + int - 1 + (1|gr(cl)*ar0(t)), + data = df, + family = stats::binomial() + ) > des$update_parameters(cov.pars = c(0.1,0.9)) > > ## ------------------------------------------------ > ## Method `Model$power` > ## ------------------------------------------------ > > ## Don't show: > setParallel(FALSE) # for the CRAN check > ## End(Don't show) > df <- nelder(~(cl(10)*t(5)) > ind(10)) > df$int <- 0 > df[df$cl > 5, 'int'] <- 1 > des <- Model$new( + formula = ~ factor(t) + int - 1 + (1|gr(cl)) + (1|gr(cl,t)), + covariance = c(0.05,0.1), + mean = c(rep(0,5),0.6), + data = df, + family = stats::gaussian(), + var_par = 1 + ) > des$power() #power of 0.90 for the int parameter Value SE Power b_t1 0.0 0.08803844 0.0250000 b_t2 0.0 0.07610092 0.0250000 b_t3 0.0 0.07520187 0.0250000 b_t4 0.0 0.07712779 0.0250000 b_t5 0.0 0.07901103 0.0250000 b_int 0.6 0.09765424 0.9999857 > > ## ------------------------------------------------ > ## Method `Model$fit` > ## ------------------------------------------------ > > # Simulated trial data example using REML > set.seed(123) > data(SimTrial,package = "glmmrBase") > fit1 <- Model$new( + formula = y ~ int + factor(t) - 1 + (1|grlog(cl)*ar0log(t)), + data = SimTrial, + family = gaussian() + )$fit(reml = TRUE) Error: Exponent fail: nan^1.000000 Execution halted
checking PDF version of manual ... [8s/18s] OK
DONE Status: 1 ERROR
using check arguments '--no-clean-on-error '