Check results for 'glmmrBase'

using R version 4.5.2 Patched (2026-01-31 r89382)
using platform: x86_64-apple-darwin20
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this is package ‘glmmrBase’ version ‘1.3.0’
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[4s/4s] 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$power() #power of 0.90 for the int parameter Value SE Power b_t1 0.0 0.1843909 0.025000 b_t2 0.0 0.1843909 0.025000 b_t3 0.0 0.1843909 0.025000 b_t4 0.0 0.1843909 0.025000 b_t5 0.0 0.1843909 0.025000 b_int 0.6 0.1897367 0.885379 > > ## ------------------------------------------------ > ## Method `Model$MCML` > ## ------------------------------------------------ > > ## Not run: > ##D # Simulated trial data example > ##D data(SimTrial,package = "glmmrBase") > ##D model <- Model$new( > ##D formula = y ~ int + factor(t) - 1 + (1|gr(cl)*ar1(t)), > ##D data = SimTrial, > ##D family = gaussian() > ##D ) > ##D glm3 <- model$MCML() > ##D > ##D # Salamanders data example > ##D data(Salamanders,package="glmmrBase") > ##D model <- Model$new( > ##D mating~fpop:mpop-1+(1|gr(mnum))+(1|gr(fnum)), > ##D data = Salamanders, > ##D family = binomial() > ##D ) > ##D > ##D # use MCEM + REML with 500 sampling iterations > ##D glm2 <- model$MCML(method = "mcem", iter.sampling = 500, reml = TRUE) > ##D > ##D # as an alternative, we will specify the variance parameters on the > ##D # log scale and use a fast fitting algorithm > ##D # we will use two newton-raphson steps, and Normal approximation posteriors with > ##D # conjugate gradient descent > ##D # the maximum number of iterations is increased as it takes 100-110 in this example > ##D # we can also chain together the functions > ##D glm3 <- Model$new( > ##D mating~fpop:mpop-1+(1|grlog(mnum))+(1|grlog(fnum)), > ##D data = Salamanders, > ##D family = binomial() > ##D )$MCML(method = "mcnr2", mcmc.pkg = "analytic", iter.sampling = 50, max.iter = 150) > ##D > ##D # Example using simulated data > ##D #create example data with six clusters, five time periods, and five people per cluster-period > ##D df <- nelder(~(cl(6)*t(5)) > ind(5)) > ##D # parallel trial design intervention indicator > ##D df$int <- 0 > ##D df[df$cl > 3, 'int'] <- 1 > ##D # specify parameter values in the call for the data simulation below > ##D des <- Model$new( > ##D formula= ~ factor(t) + int - 1 +(1|gr(cl)*ar0(t)), > ##D covariance = c(0.05,0.7), > ##D mean = c(rep(0,5),0.2), > ##D data = df, > ##D family = gaussian() > ##D ) > ##D ysim <- des$sim_data() # simulate some data from the model > ##D fit1 <- des$MCML(y = ysim) # Default model fitting with SAEM > ##D # use MCNR instead and stop when parameter values are within 1e-2 on successive iterations > ##D fit2 <- des$MCML(y = ysim, method="mcnr",tol=1e-2,conv.criterion = 1) > ##D > ##D # Non-linear model fitting example using the example provided by nlmer in lme4 > ##D data(Orange, package = "lme4") > ##D > ##D # the lme4 example: > ##D startvec <- c(Asym = 200, xmid = 725, scal = 350) > ##D (nm1 <- lme4::nlmer(circumference ~ SSlogis(age, Asym, xmid, scal) ~ Asym|Tree, > ##D Orange, start = startvec)) > ##D > ##D Orange <- as.data.frame(Orange) > ##D Orange$Tree <- as.numeric(Orange$Tree) > ##D > ##D # Here we can specify the model as a function. > ##D > ##D model <- Model$new( > ##D circumference ~ Asym/(1 + exp((xmid - (age))/scal)) - 1 + (Asym|gr(Tree)), > ##D data = Orange, > ##D family = gaussian(), > ##D mean = c(200,725,350), > ##D covariance = c(500), > ##D var_par = 50 > ##D ) > ##D > ##D # for this example, we will use MCEM with adaptive MCMC sample sizes > ##D > ##D nfit <- model$MCML(method = "mcem.adapt", iter.sampling = 1000) > ##D > ##D summary(nfit) > ##D summary(nm1) > ##D > ##D > ## End(Not run) > > ## ------------------------------------------------ > ## Method `Model$fit` > ## ------------------------------------------------ > > # Simulated trial data example using REML > data(SimTrial,package = "glmmrBase") > fit1 <- Model$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
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DONE Status: 1 ERROR
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