tests/testthat/tests.simulation.R
In rpsychologist/powerlmm: Power Analysis for Longitudinal Multilevel Models
p <- study_parameters(n1 = 10,
n2 = 10,
n3 = 5,
sigma_subject_intercept = 1.44,
icc_pre_cluster = 0,
sigma_subject_slope = 0.2,
icc_slope = 0.05,
sigma_error = 1.44,
cohend = 0.5)
# munge_results -----------------------------------------------------------
test_that("munge_results", {
set.seed(5)
formula <- sim_formula_compare("correct" = sim_formula("y ~ treatment * time + (1 + time | subject) +
(0 + time | cluster)"))
res <- lapply(1:3, simulate_,
paras = p,
satterthwaite = FALSE,
CI = FALSE,
formula = formula)
munged <- munge_results(list(res = res))
# subject_slope
tmp <- rep(NA, 3)
for(i in 1:3) {
x <- res[[i]]$correct$RE
tmp[i] <- x[x$parameter == "subject_time", "vcov"]
}
x <- munged$res$correct$RE
x <- x[x$parameter == "subject_slope", "vcov"]
expect_equal(x, tmp)
# cluster_slope
tmp <- rep(NA, 3)
for(i in 1:3) {
x <- res[[i]]$correct$RE
tmp[i] <- x[x$parameter == "cluster_time", "vcov"]
}
x <- munged$res$correct$RE
x <- x[x$parameter == "cluster_slope", "vcov"]
expect_equal(x, tmp)
# time:treatment
# subject_slope
tmp <- rep(NA, 3)
for(i in 1:3) {
x <- res[[i]]$correct$FE
tmp[i] <- x[x$parameter == "treatment:time", "estimate"]
}
x <- munged$res$correct$FE
x <- x[x$parameter == "treatment:time", "estimate"]
expect_equal(x, tmp)
})
# extract_results ---------------------------------------------------------
test_that("extract results", {
set.seed(34534)
d <- simulate_data(p)
fit <- lme4::lmer(y ~ treatment * time + (1 + time | subject) +
(0 + time | cluster), data = d)
tmp <- extract_results(list(list("fit" = fit)), CI = FALSE, df_bw = 8, tot_n = 100, sim = 1)
x <- tmp[[1]]$RE
expect_equal(x$vcov, c(2.330233, 0.032569, 0.008725, 2.070966, -0.193),
tolerance = 0.001)
pnames <- c( "subject_(Intercept)", "subject_time", "cluster_time",
"Residual_NA", "subject_(Intercept)_time")
expect_equal(x$parameter, pnames)
# satterth
tmp <- extract_results(list(list("fit" = fit, "test" = "treatment:time")), satterthwaite = TRUE, CI = FALSE, df_bw = 8, tot_n = 100, sim = 1)
expect_false(is.na(tmp[[1]]$FE[4,"pval"]))
expect_false(is.na(tmp[[1]]$FE[4,"df"]))
})
test_that("simulation summary", {
p <- update(p, fixed_intercept = 4.4,
fixed_slope = -0.22)
set.seed(5446)
formula <- sim_formula("y ~ treatment * time + (1 + time | subject) + (0 + time | cluster)")
res <- simulate(p, nsim = 3, formula = formula, satterthwaite = FALSE,
progress = FALSE)
tmp <- summary(res)
# print
expect_output(print(tmp), "Model: default")
expect_output(print(tmp), "Random effects")
expect_output(print(tmp), "Fixed effects")
expect_output(print(tmp), "Number of simulations: 3")
expect_output(print(tmp), "Total number of subjects")
expect_error(summary(res, para = c("time", "time:treatment")), "'para' must have length equal to 1")
tmp2 <- summary(res, para = "treatment:time")
expect_output(print(tmp2), "Model: summary")
expect_output(print(tmp2), "Fixed effects: 'treatment:time'")
expect_output(print(tmp2), "model M_est theta")
expect_output(print(tmp2), "Total number of subjects: 100")
tmp2 <- summary(res, para = "cluster_slope")
expect_output(print(tmp2), "Model: summary")
expect_output(print(tmp2), "Random effects: 'cluster_slope'")
expect_output(print(tmp2), "model M_est theta")
expect_output(print(tmp2), "Total number of subjects: 100")
# params
x <- as.character(tmp$summary$default$FE$parameter)
expect_equal(x, c("(Intercept)", "treatment", "time", "treatment:time"))
# theta
expect_equal(tmp$summary$default$FE$theta, c(4.4, 0, -0.22, 0.1131371), tolerance = 0.00001)
# Est
est <- c(res$res$default$FE[c(4,8,12), "estimate"])
y <- mean(est)
expect_equal(tmp$summary$default$FE[4, "M_est"], y, tolerance = 0.001)
# SE
se <- c(res$res$default$FE[c(4,8,12), "se"])
y <- mean(se)
expect_equal(tmp$summary$default$FE[4, "M_se"], y, tolerance = 0.001)
# Emperical SE
expect_equal(tmp$summary$default$FE[4,"SD_est"], sd(est), tolerance = 0.001)
# Subject_slope
est <- res$res$default$RE
est <- est[est$parameter == "subject_slope", "vcov"]
expect_equal(tmp$summary$default$RE[2, "M_est"], mean(est), tolerance = 0.001)
expect_equal(tmp$summary$default$RE[2, "prop_zero"], mean(abs(est - 0) < 0.00001),
tolerance = 0.001)
# Subject intercept_slope cor
est <- res$res$default$RE
est <- est[est$parameter == "cor_subject", "vcov"]
expect_equal(tmp$summary$default$RE[5, "M_est"], mean(est), tolerance = 0.001)
# Cluster slope
est <- res$res$default$RE
est <- est[est$parameter == "cluster_slope", "vcov"]
expect_equal(tmp$summary$default$RE[3, "M_est"], mean(est), tolerance = 0.001)
expect_equal(tmp$summary$default$RE[3, "prop_zero"], mean(abs(est - 0) < 0.00001),
tolerance = 0.001)
# df_bw
df <- res$res$default$FE$df_bw
expect_equal(df[!is.na(df)], c(8,8,8))
})
test_that("simulate with NA para", {
set.seed(45446)
p <- study_parameters(n1 = 3,
n2 = 5,
n3 = 3,
icc_pre_subject = 0.5,
icc_pre_cluster = NA,
var_ratio = 0.02,
icc_slope = 0.05,
cohend = 0
)
res <- simulate(p,
nsim = 2,
formula = sim_formula("y ~ time * treatment + (1 | subject) + (1 | cluster)"))
res
x <- summary(res)
x <- x$summary$default$RE
# cluster_intercept is NA, but still in moddel formula
# theta should be 0
expect_true(x[x$parameter == "cluster_intercept", "theta"] == 0)
})
test_that("simulation summary alpha", {
set.seed(5446)
formula <- sim_formula("y ~ treatment * time + (1 | subject)")
res <- simulate(p, nsim = 10, formula = formula, satterthwaite = FALSE,
progress = FALSE)
tmp <- summary(res)[[1]]$default$FE[, "Power"]
tmp2 <- summary(res, alpha = 0.5)[[1]]$default$FE[, "Power"]
expect_true(all(tmp < tmp2))
})
test_that("simulation partially nested", {
set.seed(45443)
p <- study_parameters(n1 = 11,
n2 = 10,
n3 = 4,
sigma_subject_intercept = 1.44,
icc_pre_cluster = 0,
sigma_subject_slope = 0.2,
cor_subject = -0.5,
icc_slope = 0.05,
sigma_error = 1.44,
partially_nested = TRUE,
cohend = -0.5)
##
formula <- sim_formula("y ~ treatment * time + (1 + time | subject) + (0 + treatment:time | cluster)")
res <- simulate(p, nsim = 3, formula = formula, satterthwaite = FALSE,
progress = FALSE, cores = 1, save = FALSE)
expect_error(summary(res), NA)
# df_bw
df <- res$res$default$FE$df_bw
expect_equal(df[!is.na(df)], c(3,3,3))
##
formula <- sim_formula("y ~ treatment * time + (1 + time | subject) + (1 + treatment:time | cluster)")
res <- simulate(p, nsim = 3, formula = formula, satterthwaite = FALSE,
progress = FALSE, cores = 1, save = FALSE)
expect_error(summary(res), NA)
##
formula <- sim_formula("y ~ treatment * time + (1 + time | subject) + (0 + time:treatment | cluster)")
res <- simulate(p, nsim = 3, formula = formula, satterthwaite = FALSE,
progress = FALSE, cores = 1, save = FALSE)
expect_error(summary(res), NA)
##
formula <- sim_formula("y ~ treatment * time + (1 + time | subject) + (1 + time:treatment | cluster)")
res <- simulate(p, nsim = 3, formula = formula, satterthwaite = FALSE,
progress = FALSE, cores = 1, save = FALSE)
expect_error(summary(res), NA)
})
test_that("simulation random n2", {
set.seed(5447)
p <- study_parameters(n1 = 11,
n2 = unequal_clusters(func = rnorm(5, 0, 10)),
sigma_subject_intercept = 1.44,
icc_pre_cluster = 0,
sigma_subject_slope = 0.2,
cor_subject = -0.5,
icc_slope = 0.05,
sigma_error = 1.44,
partially_nested = TRUE,
cohend = -0.5)
res <- simulate(p, nsim = 3, satterthwaite = FALSE,
progress = FALSE)
tmp <- res$res$default$tot_n[,1]
expect_gt(length(unique(tmp)), 1)
# df_bw
df <- res$res$default$FE$df_bw
expect_equal(df[!is.na(df)], c(4,4,4))
expect_error(summary(res), NA)
})
test_that("simulation random n2 some zero", {
set.seed(5446)
p <- study_parameters(n1 = 11,
n2 = unequal_clusters(func = rnorm(10, 0, 10), replace = 0),
sigma_subject_intercept = 1.44,
icc_pre_cluster = 0,
sigma_subject_slope = 0.2,
cor_subject = -0.5,
icc_slope = 0.05,
sigma_error = 1.44,
partially_nested = TRUE,
cohend = -0.5)
res <- simulate(p, nsim = 3, satterthwaite = FALSE,
progress = FALSE)
tmp <- res$res$default$tot_n[,1]
expect_length(unique(tmp), 3)
# df_bw
df <- res$res$default$FE$df_bw
expect_gt(length(unique(df[!is.na(df)])), 1)
expect_error(summary(res), NA)
})
# Test simulations run without error
test_that("Simulation runs, unequal_clusters", {
p <- study_parameters(n1 = 3,
n2 = unequal_clusters(15,15),
n3 = 2,
sigma_subject_intercept = 1.44,
icc_pre_cluster = 0,
sigma_subject_slope = 0.2,
icc_slope = 0.05,
sigma_error = 1.44,
cohend = 0.5)
f <- sim_formula("y ~ treatment * time + (1 + time | subject) + (0 + time | cluster)")
res <- simulate(p, nsim = 2, formula = f, satterthwaite = FALSE,
progress = FALSE)
expect_is(res$res$default$FE[1,2], "numeric")
})
# Test simulations run without error with CI
test_that("Simulation runs, unequal_clusters", {
p <- study_parameters(n1 = 3,
n2 = 10,
n3 = 2,
sigma_subject_intercept = 1.44,
icc_pre_cluster = 0,
sigma_subject_slope = 0.2,
icc_slope = 0.05,
sigma_error = 1.44,
cohend = 0.5)
f <- sim_formula("y ~ treatment * time + (1 + time | subject) + (0 + time | cluster)", test = "treatment:time")
res <- simulate(p, nsim = 2, formula = f, satterthwaite = TRUE, CI = TRUE,
progress = FALSE)
expect_is(res$res$default$FE[4, "CI_lwr"], "numeric")
expect_error(summary(res), NA)
})
test_that("Simulation runs, dropout", {
p <- study_parameters(n1 = 5,
n2 = unequal_clusters(15,15),
n3 = 2,
sigma_subject_intercept = 1.44,
icc_pre_cluster = 0,
sigma_subject_slope = 0.2,
icc_slope = 0.05,
sigma_error = 1.44,
dropout = dropout_weibull(0.2, 1),
cohend = 0.5)
f <- sim_formula("y ~ treatment * time + (1 + time | subject) + (0 + time | cluster)")
res <- simulate(p, nsim = 2, formula = f, satterthwaite = TRUE,
progress = FALSE)
expect_is(res$res$default$FE[1,2], "numeric")
})
test_that("sim data_transform", {
p <- study_parameters(n1 = 3,
n2 = 10,
n3 = 2,
sigma_subject_intercept = 1.44,
icc_pre_cluster = 0,
sigma_subject_slope = 0.2,
icc_slope = 0.05,
sigma_error = 1.44,
cohend = 0.5)
f <- sim_formula("y ~ treatment + (1 | cluster)", data_transform = transform_to_posttest, test = "treatment")
res <- simulate(p, nsim = 2, formula = f)
x <- summary(res)
expect_is(x, "plcp_sim_summary")
expect_output(print(x), "^Model: default")
## satterth and CI
res <- simulate(p, nsim = 2, formula = f, CI = TRUE, satterthwaite = TRUE)
x <- summary(res)
expect_is(x, "plcp_sim_summary")
expect_output(print(x), "^Model: default")
})
rpsychologist/powerlmm documentation built on May 11, 2023, 12:24 a.m.
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p <- study_parameters(n1 = 10,
n2 = 10,
n3 = 5,
sigma_subject_intercept = 1.44,
icc_pre_cluster = 0,
sigma_subject_slope = 0.2,
icc_slope = 0.05,
sigma_error = 1.44,
cohend = 0.5)
# munge_results -----------------------------------------------------------
test_that("munge_results", {
set.seed(5)
formula <- sim_formula_compare("correct" = sim_formula("y ~ treatment * time + (1 + time | subject) +
(0 + time | cluster)"))
res <- lapply(1:3, simulate_,
paras = p,
satterthwaite = FALSE,
CI = FALSE,
formula = formula)
munged <- munge_results(list(res = res))
# subject_slope
tmp <- rep(NA, 3)
for(i in 1:3) {
x <- res[[i]]$correct$RE
tmp[i] <- x[x$parameter == "subject_time", "vcov"]
}
x <- munged$res$correct$RE
x <- x[x$parameter == "subject_slope", "vcov"]
expect_equal(x, tmp)
# cluster_slope
tmp <- rep(NA, 3)
for(i in 1:3) {
x <- res[[i]]$correct$RE
tmp[i] <- x[x$parameter == "cluster_time", "vcov"]
}
x <- munged$res$correct$RE
x <- x[x$parameter == "cluster_slope", "vcov"]
expect_equal(x, tmp)
# time:treatment
# subject_slope
tmp <- rep(NA, 3)
for(i in 1:3) {
x <- res[[i]]$correct$FE
tmp[i] <- x[x$parameter == "treatment:time", "estimate"]
}
x <- munged$res$correct$FE
x <- x[x$parameter == "treatment:time", "estimate"]
expect_equal(x, tmp)
})
# extract_results ---------------------------------------------------------
test_that("extract results", {
set.seed(34534)
d <- simulate_data(p)
fit <- lme4::lmer(y ~ treatment * time + (1 + time | subject) +
(0 + time | cluster), data = d)
tmp <- extract_results(list(list("fit" = fit)), CI = FALSE, df_bw = 8, tot_n = 100, sim = 1)
x <- tmp[[1]]$RE
expect_equal(x$vcov, c(2.330233, 0.032569, 0.008725, 2.070966, -0.193),
tolerance = 0.001)
pnames <- c( "subject_(Intercept)", "subject_time", "cluster_time",
"Residual_NA", "subject_(Intercept)_time")
expect_equal(x$parameter, pnames)
# satterth
tmp <- extract_results(list(list("fit" = fit, "test" = "treatment:time")), satterthwaite = TRUE, CI = FALSE, df_bw = 8, tot_n = 100, sim = 1)
expect_false(is.na(tmp[[1]]$FE[4,"pval"]))
expect_false(is.na(tmp[[1]]$FE[4,"df"]))
})
test_that("simulation summary", {
p <- update(p, fixed_intercept = 4.4,
fixed_slope = -0.22)
set.seed(5446)
formula <- sim_formula("y ~ treatment * time + (1 + time | subject) + (0 + time | cluster)")
res <- simulate(p, nsim = 3, formula = formula, satterthwaite = FALSE,
progress = FALSE)
tmp <- summary(res)
# print
expect_output(print(tmp), "Model: default")
expect_output(print(tmp), "Random effects")
expect_output(print(tmp), "Fixed effects")
expect_output(print(tmp), "Number of simulations: 3")
expect_output(print(tmp), "Total number of subjects")
expect_error(summary(res, para = c("time", "time:treatment")), "'para' must have length equal to 1")
tmp2 <- summary(res, para = "treatment:time")
expect_output(print(tmp2), "Model: summary")
expect_output(print(tmp2), "Fixed effects: 'treatment:time'")
expect_output(print(tmp2), "model M_est theta")
expect_output(print(tmp2), "Total number of subjects: 100")
tmp2 <- summary(res, para = "cluster_slope")
expect_output(print(tmp2), "Model: summary")
expect_output(print(tmp2), "Random effects: 'cluster_slope'")
expect_output(print(tmp2), "model M_est theta")
expect_output(print(tmp2), "Total number of subjects: 100")
# params
x <- as.character(tmp$summary$default$FE$parameter)
expect_equal(x, c("(Intercept)", "treatment", "time", "treatment:time"))
# theta
expect_equal(tmp$summary$default$FE$theta, c(4.4, 0, -0.22, 0.1131371), tolerance = 0.00001)
# Est
est <- c(res$res$default$FE[c(4,8,12), "estimate"])
y <- mean(est)
expect_equal(tmp$summary$default$FE[4, "M_est"], y, tolerance = 0.001)
# SE
se <- c(res$res$default$FE[c(4,8,12), "se"])
y <- mean(se)
expect_equal(tmp$summary$default$FE[4, "M_se"], y, tolerance = 0.001)
# Emperical SE
expect_equal(tmp$summary$default$FE[4,"SD_est"], sd(est), tolerance = 0.001)
# Subject_slope
est <- res$res$default$RE
est <- est[est$parameter == "subject_slope", "vcov"]
expect_equal(tmp$summary$default$RE[2, "M_est"], mean(est), tolerance = 0.001)
expect_equal(tmp$summary$default$RE[2, "prop_zero"], mean(abs(est - 0) < 0.00001),
tolerance = 0.001)
# Subject intercept_slope cor
est <- res$res$default$RE
est <- est[est$parameter == "cor_subject", "vcov"]
expect_equal(tmp$summary$default$RE[5, "M_est"], mean(est), tolerance = 0.001)
# Cluster slope
est <- res$res$default$RE
est <- est[est$parameter == "cluster_slope", "vcov"]
expect_equal(tmp$summary$default$RE[3, "M_est"], mean(est), tolerance = 0.001)
expect_equal(tmp$summary$default$RE[3, "prop_zero"], mean(abs(est - 0) < 0.00001),
tolerance = 0.001)
# df_bw
df <- res$res$default$FE$df_bw
expect_equal(df[!is.na(df)], c(8,8,8))
})
test_that("simulate with NA para", {
set.seed(45446)
p <- study_parameters(n1 = 3,
n2 = 5,
n3 = 3,
icc_pre_subject = 0.5,
icc_pre_cluster = NA,
var_ratio = 0.02,
icc_slope = 0.05,
cohend = 0
)
res <- simulate(p,
nsim = 2,
formula = sim_formula("y ~ time * treatment + (1 | subject) + (1 | cluster)"))
res
x <- summary(res)
x <- x$summary$default$RE
# cluster_intercept is NA, but still in moddel formula
# theta should be 0
expect_true(x[x$parameter == "cluster_intercept", "theta"] == 0)
})
test_that("simulation summary alpha", {
set.seed(5446)
formula <- sim_formula("y ~ treatment * time + (1 | subject)")
res <- simulate(p, nsim = 10, formula = formula, satterthwaite = FALSE,
progress = FALSE)
tmp <- summary(res)[[1]]$default$FE[, "Power"]
tmp2 <- summary(res, alpha = 0.5)[[1]]$default$FE[, "Power"]
expect_true(all(tmp < tmp2))
})
test_that("simulation partially nested", {
set.seed(45443)
p <- study_parameters(n1 = 11,
n2 = 10,
n3 = 4,
sigma_subject_intercept = 1.44,
icc_pre_cluster = 0,
sigma_subject_slope = 0.2,
cor_subject = -0.5,
icc_slope = 0.05,
sigma_error = 1.44,
partially_nested = TRUE,
cohend = -0.5)
##
formula <- sim_formula("y ~ treatment * time + (1 + time | subject) + (0 + treatment:time | cluster)")
res <- simulate(p, nsim = 3, formula = formula, satterthwaite = FALSE,
progress = FALSE, cores = 1, save = FALSE)
expect_error(summary(res), NA)
# df_bw
df <- res$res$default$FE$df_bw
expect_equal(df[!is.na(df)], c(3,3,3))
##
formula <- sim_formula("y ~ treatment * time + (1 + time | subject) + (1 + treatment:time | cluster)")
res <- simulate(p, nsim = 3, formula = formula, satterthwaite = FALSE,
progress = FALSE, cores = 1, save = FALSE)
expect_error(summary(res), NA)
##
formula <- sim_formula("y ~ treatment * time + (1 + time | subject) + (0 + time:treatment | cluster)")
res <- simulate(p, nsim = 3, formula = formula, satterthwaite = FALSE,
progress = FALSE, cores = 1, save = FALSE)
expect_error(summary(res), NA)
##
formula <- sim_formula("y ~ treatment * time + (1 + time | subject) + (1 + time:treatment | cluster)")
res <- simulate(p, nsim = 3, formula = formula, satterthwaite = FALSE,
progress = FALSE, cores = 1, save = FALSE)
expect_error(summary(res), NA)
})
test_that("simulation random n2", {
set.seed(5447)
p <- study_parameters(n1 = 11,
n2 = unequal_clusters(func = rnorm(5, 0, 10)),
sigma_subject_intercept = 1.44,
icc_pre_cluster = 0,
sigma_subject_slope = 0.2,
cor_subject = -0.5,
icc_slope = 0.05,
sigma_error = 1.44,
partially_nested = TRUE,
cohend = -0.5)
res <- simulate(p, nsim = 3, satterthwaite = FALSE,
progress = FALSE)
tmp <- res$res$default$tot_n[,1]
expect_gt(length(unique(tmp)), 1)
# df_bw
df <- res$res$default$FE$df_bw
expect_equal(df[!is.na(df)], c(4,4,4))
expect_error(summary(res), NA)
})
test_that("simulation random n2 some zero", {
set.seed(5446)
p <- study_parameters(n1 = 11,
n2 = unequal_clusters(func = rnorm(10, 0, 10), replace = 0),
sigma_subject_intercept = 1.44,
icc_pre_cluster = 0,
sigma_subject_slope = 0.2,
cor_subject = -0.5,
icc_slope = 0.05,
sigma_error = 1.44,
partially_nested = TRUE,
cohend = -0.5)
res <- simulate(p, nsim = 3, satterthwaite = FALSE,
progress = FALSE)
tmp <- res$res$default$tot_n[,1]
expect_length(unique(tmp), 3)
# df_bw
df <- res$res$default$FE$df_bw
expect_gt(length(unique(df[!is.na(df)])), 1)
expect_error(summary(res), NA)
})
# Test simulations run without error
test_that("Simulation runs, unequal_clusters", {
p <- study_parameters(n1 = 3,
n2 = unequal_clusters(15,15),
n3 = 2,
sigma_subject_intercept = 1.44,
icc_pre_cluster = 0,
sigma_subject_slope = 0.2,
icc_slope = 0.05,
sigma_error = 1.44,
cohend = 0.5)
f <- sim_formula("y ~ treatment * time + (1 + time | subject) + (0 + time | cluster)")
res <- simulate(p, nsim = 2, formula = f, satterthwaite = FALSE,
progress = FALSE)
expect_is(res$res$default$FE[1,2], "numeric")
})
# Test simulations run without error with CI
test_that("Simulation runs, unequal_clusters", {
p <- study_parameters(n1 = 3,
n2 = 10,
n3 = 2,
sigma_subject_intercept = 1.44,
icc_pre_cluster = 0,
sigma_subject_slope = 0.2,
icc_slope = 0.05,
sigma_error = 1.44,
cohend = 0.5)
f <- sim_formula("y ~ treatment * time + (1 + time | subject) + (0 + time | cluster)", test = "treatment:time")
res <- simulate(p, nsim = 2, formula = f, satterthwaite = TRUE, CI = TRUE,
progress = FALSE)
expect_is(res$res$default$FE[4, "CI_lwr"], "numeric")
expect_error(summary(res), NA)
})
test_that("Simulation runs, dropout", {
p <- study_parameters(n1 = 5,
n2 = unequal_clusters(15,15),
n3 = 2,
sigma_subject_intercept = 1.44,
icc_pre_cluster = 0,
sigma_subject_slope = 0.2,
icc_slope = 0.05,
sigma_error = 1.44,
dropout = dropout_weibull(0.2, 1),
cohend = 0.5)
f <- sim_formula("y ~ treatment * time + (1 + time | subject) + (0 + time | cluster)")
res <- simulate(p, nsim = 2, formula = f, satterthwaite = TRUE,
progress = FALSE)
expect_is(res$res$default$FE[1,2], "numeric")
})
test_that("sim data_transform", {
p <- study_parameters(n1 = 3,
n2 = 10,
n3 = 2,
sigma_subject_intercept = 1.44,
icc_pre_cluster = 0,
sigma_subject_slope = 0.2,
icc_slope = 0.05,
sigma_error = 1.44,
cohend = 0.5)
f <- sim_formula("y ~ treatment + (1 | cluster)", data_transform = transform_to_posttest, test = "treatment")
res <- simulate(p, nsim = 2, formula = f)
x <- summary(res)
expect_is(x, "plcp_sim_summary")
expect_output(print(x), "^Model: default")
## satterth and CI
res <- simulate(p, nsim = 2, formula = f, CI = TRUE, satterthwaite = TRUE)
x <- summary(res)
expect_is(x, "plcp_sim_summary")
expect_output(print(x), "^Model: default")
})
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