# context("simulate power model directly")
#
# test_that("lm model specification", {
# fixed <- ~ 1 + act + diff + numCourse + act:numCourse
# fixed_param <- c(0.5, 1.1, 0.6, 0.9, 1.1)
# cov_param <- list(dist_fun = c('rnorm', 'rnorm', 'rnorm'),
# var_type = c("single", "single", "single"),
# opts = list(list(mean = 0, sd = 2),
# list(mean = 0, sd = 2),
# list(mean = 0, sd = 1)))
# n <- 150
# error_var <- 20
# with_err_gen <- 'rnorm'
# pow_param <- c('(Intercept)', 'act', 'diff', 'numCourse')
# alpha <- .01
# pow_dist <- "t"
# pow_tail <- 2
# replicates <- 2
# expect_error(sim_pow(fixed = fixed, fixed_param = fixed_param, cov_param = cov_param,
# n = n, error_var = error_var, with_err_gen = with_err_gen,
# data_str = "single", pow_param = pow_param, alpha = alpha,
# pow_dist = pow_dist, pow_tail = pow_tail,
# replicates = replicates, raw_power = FALSE,
# lm_fit_mod = TRUE),
# 'lm_fit_mod must be a formula to pass to lm')
#
# lm_fit_mod <- sim_data ~ 1 + act + diff + numCourse
# power_out <- sim_pow(fixed = fixed, fixed_param = fixed_param, cov_param = cov_param,
# n = n, error_var = error_var, with_err_gen = with_err_gen,
# data_str = "single", pow_param = pow_param, alpha = alpha,
# pow_dist = pow_dist, pow_tail = pow_tail,
# replicates = replicates, raw_power = FALSE,
# lm_fit_mod = lm_fit_mod)
# expect_equal(nrow(power_out), 4)
#
# pow_param <- c('(Intercept)', 'act')
# lm_fit_mod <- sim_data ~ 1 + act
# power_out <- sim_pow(fixed = fixed, fixed_param = fixed_param, cov_param = cov_param,
# n = n, error_var = error_var, with_err_gen = with_err_gen,
# data_str = "single", pow_param = pow_param, alpha = alpha,
# pow_dist = pow_dist, pow_tail = pow_tail,
# replicates = replicates, raw_power = FALSE,
# lm_fit_mod = lm_fit_mod)
# expect_equal(nrow(power_out), 2)
# })
#
# test_that('glm model specification', {
# fixed <- ~ 1 + act + diff + numCourse + act:numCourse
# fixed_param <- c(0.5, 1.1, 0.6, 0.9, 1.1)
# cov_param <- list(dist_fun = c('rnorm', 'rnorm', 'rnorm'),
# var_type = c("single", "single", "single"),
# opts = list(list(mean = 0, sd = 2),
# list(mean = 0, sd = 2),
# list(mean = 0, sd = 1)))
# n <- 150
# pow_param <- c('(Intercept)', 'act', 'diff', 'numCourse')
# alpha <- .01
# pow_dist <- "t"
# pow_tail <- 2
# replicates <- 2
# expect_error(sim_pow_glm(fixed = fixed, fixed_param = fixed_param, cov_param = cov_param,
# n = n, data_str = "single", outcome_type = 'logistic',
# pow_param = pow_param, alpha = alpha,
# pow_dist = pow_dist, pow_tail = pow_tail,
# replicates = replicates, raw_power = FALSE,
# glm_fit_mod = TRUE),
# 'glm_fit_mod must be a formula to pass to glm')
# })
#
# test_that('lme4 model specification', {
# fixed <- ~1 + time + diff + act + actClust + time:act
# random <- ~1 + time + diff
# random3 <- ~ 1 + time
# fixed_param <- c(4, 2, 6, 2.3, 7, 0)
# random_param <- list(random_var = c(7, 4, 2), rand_gen = 'rnorm')
# random_param3 <- list(random_var = c(4, 2), rand_gen = 'rnorm')
# cov_param <- list(dist_fun = c('rnorm', 'rnorm', 'rnorm'),
# var_type = c("level1", "level2", "level3"),
# opts = list(list(mean = 0, sd = 1.5),
# list(mean = 0, sd = 4),
# list(mean = 0, sd = 2)))
# k <- 10
# n <- 15
# p <- 10
# error_var <- 4
# with_err_gen <- 'rnorm'
# data_str <- "long"
# pow_param <- c('(Intercept)', 'act', 'diff')
# alpha <- .01
# pow_dist <- "t"
# pow_tail <- 2
# replicates <- 2
# expect_error(sim_pow(fixed, random, random3, fixed_param, random_param,
# random_param3, cov_param, k,n, p, error_var, with_err_gen,
# data_str = data_str, pow_param = pow_param, alpha = alpha,
# pow_dist = pow_dist, pow_tail = pow_tail,
# replicates = replicates, raw_power = FALSE,
# lme4_fit_mod = TRUE),
# 'lme4_fit_mod must be a formula to pass to lmer')
# })
#
# test_that('lme4 glm model specification', {
# fixed <- ~1 + time + diff + act + time:act
# random <- ~1 + time + diff
# fixed_param <- c(4, 2, 6, 2.3, 7)
# random_param <- list(random_var = c(7, 4, 2), rand_gen = 'rnorm')
# cov_param <- list(dist_fun = c('rnorm', 'rnorm'),
# var_type = c("level1", "level2"),
# opts = list(list(mean = 0, sd = 1.5),
# list(mean = 0, sd = 4)))
# n <- 10
# p <- 3
# data_str <- "long"
# pow_param <- c('(Intercept)', 'act', 'diff')
# alpha <- .01
# pow_dist <- "t"
# pow_tail <- 2
# replicates <- 2
# expect_error(sim_pow_glm(fixed, random, random3 = NULL, fixed_param,
# random_param, random_param3 = NULL,
# cov_param, k = NULL, n, p,
# data_str = data_str, outcome_type = 'logistic',
# pow_param = pow_param, alpha = alpha,
# pow_dist = pow_dist, pow_tail = pow_tail,
# replicates = replicates, raw_power = FALSE,
# lme4_fit_mod = TRUE),
# 'lme4_fit_mod must be a formula to pass to glmer')
# })
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