Last updated on 2026-05-11 01:51:46 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.4.0 | 362.64 | 81.43 | 444.07 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.4.0 | 307.57 | 68.22 | 375.79 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 1.4.0 | 346.00 | 121.07 | 467.07 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.4.0 | 660.00 | 131.96 | 791.96 | OK | |
| r-devel-windows-x86_64 | 1.4.0 | 291.00 | 150.00 | 441.00 | OK | |
| r-patched-linux-x86_64 | 1.4.0 | 370.40 | 94.05 | 464.45 | OK | |
| r-release-linux-x86_64 | 1.4.0 | 359.36 | 94.51 | 453.87 | OK | |
| r-release-macos-arm64 | 1.4.0 | 75.00 | 21.00 | 96.00 | ERROR | |
| r-release-macos-x86_64 | 1.4.0 | 258.00 | 227.00 | 485.00 | ERROR | |
| r-release-windows-x86_64 | 1.4.0 | 281.00 | 149.00 | 430.00 | OK | |
| r-oldrel-macos-arm64 | 1.4.0 | 77.00 | 21.00 | 98.00 | ERROR | |
| r-oldrel-macos-x86_64 | 1.4.0 | 253.00 | 169.00 | 422.00 | ERROR | |
| r-oldrel-windows-x86_64 | 1.4.0 | 369.00 | 180.00 | 549.00 | OK |
Version: 1.4.0
Check: examples
Result: ERROR
Running examples in ‘glmmrBase-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### 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$power() #power of 0.90 for the int parameter
Value SE Power
b_t1 0.0 0.07891707 0.0250000
b_t2 0.0 0.07282804 0.0250000
b_t3 0.0 0.07245347 0.0250000
b_t4 0.0 0.07410430 0.0250000
b_t5 0.0 0.07578151 0.0250000
b_int 0.6 0.08721746 0.9999996
>
> ## ------------------------------------------------
> ## Method `Model$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)
>
> # Salamanders data example
> data(Salamanders,package="glmmrBase")
> model <- Model$new(
+ mating~fpop:mpop-1+(1|grlog(mnum))+(1|grlog(fnum)),
+ data = Salamanders,
+ family = binomial()
+ )
> set.seed(125)
> fit2 <- model$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: -0.110444 -1156.98
y range: [0, 1]
offset range: [0, 0]
u_ range: [-3.02075, 2.90017]
u_mean_ range: [-1.58949, 1.34451]
u_weight_ sum: -nan, ESS: -nan
Error: Numerical error detected. See diagnostics above.
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 1.4.0
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$power() #power of 0.90 for the int parameter
Value SE Power
b_t1 0.0 0.08328203 0.0250000
b_t2 0.0 0.06840661 0.0250000
b_t3 0.0 0.07021014 0.0250000
b_t4 0.0 0.07196849 0.0250000
b_t5 0.0 0.07368489 0.0250000
b_int 0.6 0.08598979 0.9999997
>
> ## ------------------------------------------------
> ## Method `Model$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 in solve.default(M) :
system is computationally singular: reciprocal condition number = 8.43056e-46
Calls: <Anonymous> -> solve -> solve -> solve.default
Execution halted
Flavor: r-release-macos-arm64
Version: 1.4.0
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$power() #power of 0.90 for the int parameter
Value SE Power
b_t1 0.0 NaN NaN
b_t2 0.0 NaN NaN
b_t3 0.0 NaN NaN
b_t4 0.0 NaN NaN
b_t5 0.0 NaN NaN
b_int 0.6 NaN NaN
>
> ## ------------------------------------------------
> ## Method `Model$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
Flavor: r-release-macos-x86_64
Version: 1.4.0
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$power() #power of 0.90 for the int parameter
Value SE Power
b_t1 0.0 0.09242313 0.0250000
b_t2 0.0 0.07614742 0.0250000
b_t3 0.0 0.07570561 0.0250000
b_t4 0.0 0.07727120 0.0250000
b_t5 0.0 0.07887363 0.0250000
b_int 0.6 0.09079002 0.9999983
>
> ## ------------------------------------------------
> ## Method `Model$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 in solve.default(M) :
system is computationally singular: reciprocal condition number = 1.68594e-45
Calls: <Anonymous> -> solve -> solve -> solve.default
Execution halted
Flavor: r-oldrel-macos-arm64
Version: 1.4.0
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$power() #power of 0.90 for the int parameter
Value SE Power
b_t1 0.0 0.08095967 0.0250000
b_t2 0.0 0.07354064 0.0250000
b_t3 0.0 0.07312913 0.0250000
b_t4 0.0 0.07476178 0.0250000
b_t5 0.0 0.07642305 0.0250000
b_int 0.6 0.08797238 0.9999994
>
> ## ------------------------------------------------
> ## Method `Model$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
Flavor: r-oldrel-macos-x86_64