Nothing
test_that("print.BayesianMCPMod works as intented", {
expect_error(print.BayesianMCPMod())
})
test_that("print.BayesianMCP works as intented", {
expect_error(print.BayesianMCP())
data <- simulateData(
n_patients = n_patients,
dose_levels = dose_levels,
sd = sd,
mods = mods,
n_sim = n_sim
)
posterior_list <- getPosterior(
data = getModelData(data, names(mods)[1]),
prior_list = prior_list
)
contr_mat = getContr(
mods = mods,
dose_levels = dose_levels,
dose_weights = n_patients,
prior_list = prior_list
)
crit_pval = getCritProb(
mods = mods,
dose_levels = dose_levels,
dose_weights = n_patients,
alpha_crit_val = alpha_crit_val
)
b_mcp <- performBayesianMCP(
posterior_list = posterior_list,
contr = contr_mat,
crit_prob_adj = crit_pval
)
expect_s3_class(b_mcp, "BayesianMCP")
expect_no_error(print(b_mcp))
expect_type(print(b_mcp), "double")
mods <- DoseFinding::Mods(
linear = NULL,
emax = c(0.5, 1.2),
exponential = 2,
doses = c(0, 0.5, 2, 4, 8)
)
dose_levels <- c(0, 0.5, 2, 4, 8)
sd_posterior <- c(2.8, 3, 2.5, 3.5, 4)
contr_mat <- getContr(
mods = mods,
dose_levels = dose_levels,
sd_posterior = sd_posterior
)
critVal <- getCritProb(
mods = mods,
dose_weights = c(50, 50, 50, 50, 50), # reflecting the planned sample size
dose_levels = dose_levels,
alpha_crit_val = 0.6
)
prior_list <- list(
Ctrl = RBesT::mixnorm(comp1 = c(w = 1, m = 0, s = 5), sigma = 2),
DG_1 = RBesT::mixnorm(comp1 = c(w = 1, m = 1, s = 12), sigma = 2),
DG_2 = RBesT::mixnorm(comp1 = c(w = 1, m = 1.2, s = 11), sigma = 2),
DG_3 = RBesT::mixnorm(comp1 = c(w = 1, m = 1.3, s = 11), sigma = 2),
DG_4 = RBesT::mixnorm(comp1 = c(w = 1, m = 2, s = 13), sigma = 2)
)
mu <- c(0, 1, 1.5, 2, 2.5)
S_hat <- c(5, 4, 6, 7, 8)
posterior_list <- getPosterior(
prior_list = prior_list,
mu_hat = mu,
S_hat = S_hat
)
x <- performBayesianMCPMod(
posterior_list = posterior_list,
contr = contr_mat,
crit_prob_adj = critVal,
simple = FALSE,
delta = 1
)
expect_s3_class(x, "BayesianMCPMod")
expect_no_error(print(x))
expect_type(print(x), "list")
})
test_that("predict.ModelFits works as intented", {
expect_error(predict.ModelFits())
})
test_that("s3 postList functions work as intented", {
set.seed(8080)
dataset <- dplyr::filter(testdata, bname == "BRINTELLIX")
histcontrol <- dplyr::filter(dataset, dose == 0, primtime == 8, indication == "MAJOR DEPRESSIVE DISORDER",protid!=6)
##Create MAP Prior
hist_data <- data.frame(
trial = histcontrol$nctno,
est = histcontrol$rslt,
se = histcontrol$se,
sd = histcontrol$sd,
n = histcontrol$sampsize)
dose_levels <- c(0, 2.5, 5, 10)
post_test_list <- getPriorList(
hist_data = hist_data,
dose_levels = dose_levels,
robust_weight = 0.5)
expect_error(summary.postList())
expect_type(summary.postList(post_test_list), "double")
expect_error(print.postList())
expect_type(print(post_test_list), "list")
expect_type(print.postList(post_test_list), "list")
# expect_true(names(print(post_test_list)) == c("Summary of Posterior Distributions",
# "Maximum Difference to Control and Dose Group",
# "Posterior Distributions"))
})
test_that("test modelFits s3 methods", {
model_shapes <- colnames(contr_mat$contMat)
dose_levels <- as.numeric(rownames(contr_mat$contMat))
model_fits <- getModelFits(
models = model_shapes,
dose_levels = dose_levels,
posterior = posterior_list,
simple = simple)
pred <- predict(model_fits)
pred_dosage <- predict(model_fits, doses = dose_levels)
expect_type(pred, "list")
expect_true(is.null(attr(pred, "doses")))
expect_identical(attr(pred_dosage, "doses"), dose_levels)
expect_type(print(model_fits), "list")
})
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