tests/testthat/test_model_spec.r

In simglm: Simulate Models Based on the Generalized Linear Model

# 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')
# })