Last updated on 2026-01-24 19:49:58 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.2.0 | 462.27 | 163.26 | 625.53 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.2.0 | OK | ||||
| r-devel-linux-x86_64-fedora-clang | 1.2.0 | 660.00 | 270.46 | 930.46 | ERROR | |
| r-devel-linux-x86_64-fedora-gcc | 1.2.0 | 960.00 | 274.86 | 1234.86 | OK | |
| r-devel-windows-x86_64 | 1.2.0 | 369.00 | 220.00 | 589.00 | OK | |
| r-patched-linux-x86_64 | 1.1.0 | 472.16 | 180.29 | 652.45 | OK | |
| r-release-linux-x86_64 | 1.1.0 | 449.34 | 179.56 | 628.90 | OK | |
| r-release-macos-arm64 | 1.2.0 | 94.00 | 5.00 | 99.00 | OK | |
| r-release-macos-x86_64 | 1.2.0 | 302.00 | 202.00 | 504.00 | ERROR | |
| r-release-windows-x86_64 | 1.1.0 | 333.00 | 212.00 | 545.00 | OK | |
| r-oldrel-macos-arm64 | 1.2.0 | 94.00 | 12.00 | 106.00 | NOTE | |
| r-oldrel-macos-x86_64 | 1.2.0 | 310.00 | 261.00 | 571.00 | ERROR | |
| r-oldrel-windows-x86_64 | 1.2.0 | 308.00 | 273.00 | 581.00 | NOTE |
Version: 1.2.0
Check: examples
Result: 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()
+ )$fit(reml = TRUE)
>
> # Salamanders data example
> data(Salamanders,package="glmmrBase")
> model <- Model$new(
+ mating~fpop:mpop-1+(1|grlog(mnum))+(1|grlog(fnum)),
+ data = Salamanders,
+ family = binomial()
+ )
>
> fit2 <- model$fit()
ERROR: beta[0] is NaN/Inf: -nan
ERROR: beta[1] is NaN/Inf: -nan
ERROR: beta[2] is NaN/Inf: -nan
ERROR: beta[3] is NaN/Inf: -nan
ERROR: u_solve_ contains NaN
ERROR: u_weight_ contains NaN/Inf
=== CONTEXT (from beta step) ===
Dimensions: n=120, p=4, Q=20
beta: -nan -nan -nan -nan
theta: -0.175604 -792.13
y range: [0, 1]
offset range: [0, 0]
u_ range: [-3.66002, 3.35844]
u_mean_ range: [-1.57084, 1.38828]
u_weight_ sum: -nan, ESS: -nan
Error: Numerical error detected. See diagnostics above.
Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang
Version: 1.2.0
Check: examples
Result: ERROR
Running examples in ‘glmmrBase-Ex.R’ failed
The error most likely occurred in:
> ### Name: Model
> ### Title: A GLMM Model
> ### Aliases: Model
>
> ### ** Examples
>
>
> ## ------------------------------------------------
> ## Method `Model$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
Flavors: r-release-macos-x86_64, r-oldrel-macos-x86_64
Version: 1.2.0
Check: installed package size
Result: NOTE
installed size is 170.2Mb
sub-directories of 1Mb or more:
libs 168.9Mb
Flavors: r-oldrel-macos-arm64, r-oldrel-macos-x86_64, r-oldrel-windows-x86_64
Version: 1.2.0
Check: for GNU extensions in Makefiles
Result: NOTE
GNU make is a SystemRequirements.
Flavors: r-oldrel-macos-arm64, r-oldrel-macos-x86_64, r-oldrel-windows-x86_64