.runThisTest <- Sys.getenv("RunAllinsightTests") == "yes"
if (.runThisTest && requiet("brms")) {
# Model fitting -----------------------------------------------------------
m1 <- suppressWarnings(insight::download_model("brms_mixed_6"))
m2 <- insight::download_model("brms_mv_4")
m3 <- insight::download_model("brms_2")
m4 <- insight::download_model("brms_zi_3")
m5 <- insight::download_model("brms_mv_5")
m6 <- insight::download_model("brms_corr_re1")
m7 <- suppressWarnings(insight::download_model("brms_mixed_8"))
m8 <- insight::download_model("brms_ordinal_1")
# Tests -------------------------------------------------------------------
test_that("get_predicted.brmsfit: ordinal dv", {
skip_if_not_installed("bayestestR")
pred1 <- get_predicted(m8, ci = 0.95)
pred2 <- get_predicted(m8, ci_method = "hdi", ci = 0.95)
expect_true(inherits(pred1, "get_predicted"))
expect_true(inherits(pred1, "data.frame"))
expect_true(all(c("Row", "Response") %in% colnames(pred1)))
# ci_method changes intervals but not se or predicted
pred1 <- data.frame(pred1)
pred2 <- data.frame(pred2)
expect_equal(pred1$Row, pred2$Row)
expect_equal(pred1$Response, pred2$Response)
expect_equal(pred1$Predicted, pred2$Predicted)
expect_equal(pred1$SE, pred2$SE)
expect_false(mean(pred1$CI_low == pred2$CI_low) > 0.1) # most CI bounds are different
expect_false(mean(pred1$CI_high == pred2$CI_high) > 0.1) # most CI bounds are different
# compare to manual predictions
pred3 <- get_predicted(m8, centrality_function = stats::median, ci = 0.95)
manual <- rstantools::posterior_epred(m8)
manual <- apply(manual[, , 1], 2, median)
expect_equal(pred3$Predicted[1:32], manual)
manual <- rstantools::posterior_epred(m8)
manual <- apply(manual[, , 1], 2, mean)
expect_equal(pred1$Predicted[1:32], manual)
})
test_that("find_statistic", {
expect_null(find_statistic(m1))
expect_null(find_statistic(m2))
expect_null(find_statistic(m3))
expect_null(find_statistic(m4))
expect_null(find_statistic(m5))
})
test_that("n_parameters", {
expect_equal(n_parameters(m1), 65)
expect_equal(n_parameters(m1, effects = "fixed"), 5)
})
test_that("model_info", {
expect_true(model_info(m3)$is_trial)
expect_true(model_info(m5)[[1]]$is_zero_inflated)
expect_true(model_info(m5)[[1]]$is_bayesian)
})
test_that("clean_names", {
expect_identical(
clean_names(m1),
c("count", "Age", "Base", "Trt", "patient")
)
expect_identical(
clean_names(m2),
c(
"Sepal.Length",
"Sepal.Width",
"Petal.Length",
"Species"
)
)
expect_identical(clean_names(m3), c("r", "n", "treat", "c2"))
expect_identical(
clean_names(m4),
c("count", "child", "camper", "persons")
)
expect_identical(
clean_names(m5),
c(
"count",
"count2",
"child",
"camper",
"persons",
"livebait"
)
)
})
test_that("find_predictors", {
expect_identical(find_predictors(m1), list(conditional = c("Age", "Base", "Trt")))
expect_identical(
find_predictors(m1, flatten = TRUE),
c("Age", "Base", "Trt")
)
expect_identical(
find_predictors(m1, effects = "all", component = "all"),
list(
conditional = c("Age", "Base", "Trt"),
random = "patient"
)
)
expect_identical(
find_predictors(
m1,
effects = "all",
component = "all",
flatten = TRUE
),
c("Age", "Base", "Trt", "patient")
)
expect_identical(
find_predictors(m2),
list(
SepalLength = list(conditional = c(
"Petal.Length", "Sepal.Width", "Species"
)),
SepalWidth = list(conditional = "Species")
)
)
expect_identical(
find_predictors(m2, flatten = TRUE),
c("Petal.Length", "Sepal.Width", "Species")
)
expect_identical(find_predictors(m3), list(conditional = c("treat", "c2")))
expect_identical(
find_predictors(m4),
list(
conditional = c("child", "camper"),
zero_inflated = c("child", "camper")
)
)
expect_identical(
find_predictors(m4, effects = "random"),
list(random = "persons", zero_inflated_random = "persons")
)
expect_identical(find_predictors(m4, flatten = TRUE), c("child", "camper"))
expect_identical(
find_predictors(m5),
list(
count = list(
conditional = c("child", "camper"),
zero_inflated = "camper"
),
count2 = list(
conditional = c("child", "livebait"),
zero_inflated = "child"
)
)
)
})
test_that("find_response", {
expect_equal(find_response(m1, combine = TRUE), "count")
expect_equal(
find_response(m2, combine = TRUE),
c(SepalLength = "Sepal.Length", SepalWidth = "Sepal.Width")
)
expect_equal(find_response(m3, combine = TRUE), c("r", "n"))
expect_equal(find_response(m1, combine = FALSE), "count")
expect_equal(
find_response(m2, combine = FALSE),
c(SepalLength = "Sepal.Length", SepalWidth = "Sepal.Width")
)
expect_equal(find_response(m3, combine = FALSE), c("r", "n"))
expect_equal(find_response(m4, combine = FALSE), "count")
expect_equal(
find_response(m5, combine = TRUE),
c(count = "count", count2 = "count2")
)
})
test_that("get_response", {
expect_length(get_response(m1), 236)
expect_equal(ncol(get_response(m2)), 2)
expect_equal(
colnames(get_response(m2)),
c("Sepal.Length", "Sepal.Width")
)
expect_equal(ncol(get_response(m3)), 2)
expect_equal(colnames(get_response(m3)), c("r", "n"))
expect_length(get_response(m4), 250)
expect_equal(colnames(get_response(m5)), c("count", "count2"))
})
test_that("find_variables", {
expect_identical(
find_variables(m1),
list(
response = "count",
conditional = c("Age", "Base", "Trt"),
random = "patient"
)
)
expect_identical(
find_variables(m6),
list(
response = "y",
conditional = "x",
random = "id",
sigma = "x",
sigma_random = "id"
)
)
expect_identical(
find_variables(m1, effects = "fixed"),
list(
response = "count",
conditional = c("Age", "Base", "Trt")
)
)
expect_null(find_variables(m1, component = "zi"))
expect_identical(
find_variables(m2),
list(
response = c(SepalLength = "Sepal.Length", SepalWidth = "Sepal.Width"),
SepalLength = list(conditional = c(
"Petal.Length", "Sepal.Width", "Species"
)),
SepalWidth = list(conditional = "Species")
)
)
expect_identical(
find_variables(m2, flatten = TRUE),
c(
"Sepal.Length",
"Sepal.Width",
"Petal.Length",
"Species"
)
)
expect_identical(find_variables(m3), list(
response = c("r", "n"),
conditional = c("treat", "c2")
))
expect_identical(
find_variables(m4),
list(
response = "count",
conditional = c("child", "camper"),
random = "persons",
zero_inflated = c("child", "camper"),
zero_inflated_random = "persons"
)
)
expect_identical(
find_variables(m4, flatten = TRUE),
c("count", "child", "camper", "persons")
)
})
test_that("n_obs", {
expect_equal(n_obs(m1), 236)
expect_equal(n_obs(m2), 150)
expect_equal(n_obs(m3), 10)
expect_equal(n_obs(m4), 250)
expect_equal(n_obs(m5), 250)
})
test_that("find_random", {
expect_equal(find_random(m5), list(
count = list(
random = "persons",
zero_inflated_random = "persons"
),
count2 = list(
random = "persons",
zero_inflated_random = "persons"
)
))
expect_equal(find_random(m5, flatten = TRUE), "persons")
expect_equal(find_random(m6, flatten = TRUE), "id")
})
test_that("get_random", {
zinb <- get_data(m4)
expect_equal(get_random(m4), zinb[, "persons", drop = FALSE])
})
test_that("get_data", {
d <- get_data(m6)
expect_equal(nrow(d), 200)
expect_equal(ncol(d), 3)
})
test_that("find_paramaters", {
expect_equal(
find_parameters(m1),
list(
conditional = c(
"b_Intercept",
"b_Age",
"b_Base",
"b_Trt1",
"b_Base:Trt1"
),
random = c(sprintf("r_patient[%i,Intercept]", 1:59), "sd_patient__Intercept")
)
)
expect_equal(
find_parameters(m2),
structure(
list(
SepalLength = list(
conditional = c(
"b_SepalLength_Intercept",
"b_SepalLength_Petal.Length",
"b_SepalLength_Sepal.Width",
"b_SepalLength_Speciesversicolor",
"b_SepalLength_Speciesvirginica"
),
sigma = "sigma_SepalLength"
),
SepalWidth = list(
conditional = c(
"b_SepalWidth_Intercept",
"b_SepalWidth_Speciesversicolor",
"b_SepalWidth_Speciesvirginica"
),
sigma = "sigma_SepalWidth"
)
),
"is_mv" = "1"
)
)
expect_equal(
find_parameters(m4),
list(
conditional = c("b_Intercept", "b_child", "b_camper"),
random = c(sprintf("r_persons[%i,Intercept]", 1:4), "sd_persons__Intercept"),
zero_inflated = c("b_zi_Intercept", "b_zi_child", "b_zi_camper"),
zero_inflated_random = c(sprintf("r_persons__zi[%i,Intercept]", 1:4), "sd_persons__zi_Intercept")
)
)
expect_equal(
find_parameters(m5, effects = "all"),
structure(
list(
count = list(
conditional = c("b_count_Intercept", "b_count_child", "b_count_camper"),
random = c(sprintf("r_persons__count[%i,Intercept]", 1:4), "sd_persons__count_Intercept"),
zero_inflated = c("b_zi_count_Intercept", "b_zi_count_camper"),
zero_inflated_random = c(sprintf("r_persons__zi_count[%i,Intercept]", 1:4), "sd_persons__zi_count_Intercept")
),
count2 = list(
conditional = c(
"b_count2_Intercept",
"b_count2_child",
"b_count2_livebait"
),
random = c(sprintf("r_persons__count2[%i,Intercept]", 1:4), "sd_persons__count2_Intercept"),
zero_inflated = c("b_zi_count2_Intercept", "b_zi_count2_child"),
zero_inflated_random = c(sprintf("r_persons__zi_count2[%i,Intercept]", 1:4), "sd_persons__zi_count2_Intercept")
)
),
"is_mv" = "1"
)
)
})
test_that("find_paramaters", {
expect_equal(
colnames(get_parameters(m4)),
c(
"b_Intercept",
"b_child",
"b_camper",
"b_zi_Intercept",
"b_zi_child",
"b_zi_camper"
)
)
expect_equal(
colnames(get_parameters(m4, component = "zi")),
c("b_zi_Intercept", "b_zi_child", "b_zi_camper")
)
expect_equal(
colnames(get_parameters(m4, effects = "all")),
c(
"b_Intercept", "b_child", "b_camper", "r_persons[1,Intercept]",
"r_persons[2,Intercept]", "r_persons[3,Intercept]", "r_persons[4,Intercept]",
"sd_persons__Intercept", "b_zi_Intercept", "b_zi_child", "b_zi_camper",
"r_persons__zi[1,Intercept]", "r_persons__zi[2,Intercept]", "r_persons__zi[3,Intercept]",
"r_persons__zi[4,Intercept]", "sd_persons__zi_Intercept"
)
)
expect_equal(
colnames(get_parameters(m4, effects = "random", component = "conditional")),
c(
"r_persons[1,Intercept]", "r_persons[2,Intercept]", "r_persons[3,Intercept]",
"r_persons[4,Intercept]", "sd_persons__Intercept"
)
)
expect_equal(
colnames(get_parameters(m5, effects = "random", component = "conditional")),
c(
"r_persons__count[1,Intercept]", "r_persons__count[2,Intercept]",
"r_persons__count[3,Intercept]", "r_persons__count[4,Intercept]",
"sd_persons__count_Intercept", "r_persons__count2[1,Intercept]",
"r_persons__count2[2,Intercept]", "r_persons__count2[3,Intercept]",
"r_persons__count2[4,Intercept]", "sd_persons__count2_Intercept"
)
)
expect_equal(
colnames(get_parameters(m5, effects = "all", component = "all")),
c(
"b_count_Intercept", "b_count_child", "b_count_camper", "r_persons__count[1,Intercept]",
"r_persons__count[2,Intercept]", "r_persons__count[3,Intercept]",
"r_persons__count[4,Intercept]", "sd_persons__count_Intercept",
"b_zi_count_Intercept", "b_zi_count_camper", "r_persons__zi_count[1,Intercept]",
"r_persons__zi_count[2,Intercept]", "r_persons__zi_count[3,Intercept]",
"r_persons__zi_count[4,Intercept]", "sd_persons__zi_count_Intercept",
"b_count2_Intercept", "b_count2_child", "b_count2_livebait",
"r_persons__count2[1,Intercept]", "r_persons__count2[2,Intercept]",
"r_persons__count2[3,Intercept]", "r_persons__count2[4,Intercept]",
"sd_persons__count2_Intercept", "b_zi_count2_Intercept", "b_zi_count2_child",
"r_persons__zi_count2[1,Intercept]", "r_persons__zi_count2[2,Intercept]",
"r_persons__zi_count2[3,Intercept]", "r_persons__zi_count2[4,Intercept]",
"sd_persons__zi_count2_Intercept"
)
)
})
test_that("linkfun", {
expect_false(is.null(link_function(m1)))
expect_length(link_function(m2), 2)
expect_false(is.null(link_function(m3)))
expect_false(is.null(link_function(m4)))
expect_length(link_function(m5), 2)
})
test_that("linkinv", {
expect_false(is.null(link_inverse(m1)))
expect_length(link_inverse(m2), 2)
expect_false(is.null(link_inverse(m3)))
expect_false(is.null(link_inverse(m4)))
expect_length(link_inverse(m2), 2)
})
test_that("is_multivariate", {
expect_false(is_multivariate(m1))
expect_true(is_multivariate(m2))
expect_false(is_multivariate(m3))
expect_false(is_multivariate(m4))
expect_true(is_multivariate(m5))
})
test_that("find_terms", {
expect_equal(
find_terms(m2),
list(
SepalLength = list(
response = "Sepal.Length",
conditional = c("Petal.Length", "Sepal.Width", "Species")
),
SepalWidth = list(
response = "Sepal.Width",
conditional = "Species"
)
)
)
})
test_that("find_algorithm", {
expect_equal(
find_algorithm(m1),
list(
algorithm = "sampling",
chains = 1,
iterations = 500,
warmup = 250
)
)
})
test_that("get_priors", {
expect_equal(
get_priors(m7),
data.frame(
Parameter = c(
"b_Intercept", "b_Age", "b_Base", "b_Trt1", "b_Base:Trt1",
"sd_patient__Intercept", "sd_patient__Age",
"cor_patient__Intercept__Age"
),
Distribution = c(
"student_t", "student_t", "student_t",
"student_t", "student_t", "cauchy", "cauchy", "lkj"
),
Location = c(1.4, 0, 0, 0, 0, NA, NA, 1),
Scale = c(2.5, 10, 10, 10, 10, NA, NA, NA),
df = c(3, 5, 5, 5, 5, NA, NA, NA),
stringsAsFactors = FALSE
),
ignore_attr = TRUE
)
expect_equal(
get_priors(m3),
data.frame(
Parameter = c("b_Intercept", "b_treat1", "b_c2", "b_treat1:c2"),
Distribution = c("student_t", "uniform", "uniform", "uniform"),
Location = c(0, NA, NA, NA),
Scale = c(2.5, NA, NA, NA),
df = c(3, NA, NA, NA),
stringsAsFactors = FALSE
),
ignore_attr = TRUE
)
})
test_that("Issue #645", {
# apparently BH is required to fit these brms models
skip_if_not_installed("BH")
# sink() writing permission fail on some Windows CI machines
skip_on_os("windows")
void <- suppressMessages(suppressWarnings(capture.output(
mod <- brm(
silent = 2,
data = mtcars,
family = cumulative(probit),
formula = bf(
cyl ~ 1 + mpg + drat + gearnl,
gearnl ~ 0 + (1 | gear),
nl = TRUE
)
)
)))
p <- find_predictors(mod, flatten = TRUE)
d <- get_data(mod)
expect_true("gear" %in% p)
expect_true("gear" %in% colnames(d))
})
test_that("clean_parameters", {
expect_equal(
clean_parameters(m4),
structure(
list(
Parameter = c(
"b_Intercept",
"b_child",
"b_camper",
"r_persons[1,Intercept]",
"r_persons[2,Intercept]",
"r_persons[3,Intercept]",
"r_persons[4,Intercept]",
"sd_persons__Intercept",
"b_zi_Intercept",
"b_zi_child",
"b_zi_camper",
"r_persons__zi[1,Intercept]",
"r_persons__zi[2,Intercept]",
"r_persons__zi[3,Intercept]",
"r_persons__zi[4,Intercept]",
"sd_persons__zi_Intercept"
),
Effects = c(
"fixed",
"fixed",
"fixed",
"random",
"random",
"random",
"random",
"random",
"fixed",
"fixed",
"fixed",
"random",
"random",
"random",
"random",
"random"
),
Component = c(
"conditional",
"conditional",
"conditional",
"conditional",
"conditional",
"conditional",
"conditional",
"conditional",
"zero_inflated",
"zero_inflated",
"zero_inflated",
"zero_inflated",
"zero_inflated",
"zero_inflated",
"zero_inflated",
"zero_inflated"
),
Group = c(
"",
"",
"",
"Intercept: persons",
"Intercept: persons",
"Intercept: persons",
"Intercept: persons",
"SD/Cor: persons",
"",
"",
"",
"Intercept: persons",
"Intercept: persons",
"Intercept: persons",
"Intercept: persons",
"SD/Cor: persons"
),
Cleaned_Parameter = c(
"(Intercept)",
"child",
"camper",
"persons.1",
"persons.2",
"persons.3",
"persons.4",
"(Intercept)",
"(Intercept)",
"child",
"camper",
"persons.1",
"persons.2",
"persons.3",
"persons.4",
"(Intercept)"
)
),
class = c("clean_parameters", "data.frame"),
row.names = c(
NA,
-16L
)
),
ignore_attr = TRUE
)
expect_equal(
clean_parameters(m5),
structure(
list(
Parameter = c(
"b_count_Intercept",
"b_count_child",
"b_count_camper",
"b_count2_Intercept",
"b_count2_child",
"b_count2_livebait",
"r_persons__count[1,Intercept]",
"r_persons__count[2,Intercept]",
"r_persons__count[3,Intercept]",
"r_persons__count[4,Intercept]",
"sd_persons__count_Intercept",
"r_persons__count2[1,Intercept]",
"r_persons__count2[2,Intercept]",
"r_persons__count2[3,Intercept]",
"r_persons__count2[4,Intercept]",
"sd_persons__count2_Intercept",
"b_zi_count_Intercept",
"b_zi_count_camper",
"b_zi_count2_Intercept",
"b_zi_count2_child",
"r_persons__zi_count[1,Intercept]",
"r_persons__zi_count[2,Intercept]",
"r_persons__zi_count[3,Intercept]",
"r_persons__zi_count[4,Intercept]",
"sd_persons__zi_count_Intercept",
"r_persons__zi_count2[1,Intercept]",
"r_persons__zi_count2[2,Intercept]",
"r_persons__zi_count2[3,Intercept]",
"r_persons__zi_count2[4,Intercept]",
"sd_persons__zi_count2_Intercept"
),
Effects = c(
"fixed",
"fixed",
"fixed",
"fixed",
"fixed",
"fixed",
"random",
"random",
"random",
"random",
"random",
"random",
"random",
"random",
"random",
"random",
"fixed",
"fixed",
"fixed",
"fixed",
"random",
"random",
"random",
"random",
"random",
"random",
"random",
"random",
"random",
"random"
),
Component = c(
"conditional",
"conditional",
"conditional",
"conditional",
"conditional",
"conditional",
"conditional",
"conditional",
"conditional",
"conditional",
"conditional",
"conditional",
"conditional",
"conditional",
"conditional",
"conditional",
"zero_inflated",
"zero_inflated",
"zero_inflated",
"zero_inflated",
"zero_inflated",
"zero_inflated",
"zero_inflated",
"zero_inflated",
"zero_inflated",
"zero_inflated",
"zero_inflated",
"zero_inflated",
"zero_inflated",
"zero_inflated"
),
Group = c(
"",
"",
"",
"",
"",
"",
"Intercept: persons",
"Intercept: persons",
"Intercept: persons",
"Intercept: persons",
"SD/Cor: persons",
"Intercept: persons2",
"Intercept: persons2",
"Intercept: persons2",
"Intercept: persons2",
"SD/Cor: persons",
"",
"",
"",
"",
"Intercept: persons",
"Intercept: persons",
"Intercept: persons",
"Intercept: persons",
"SD/Cor: persons",
"Intercept: persons2",
"Intercept: persons2",
"Intercept: persons2",
"Intercept: persons2",
"SD/Cor: persons"
),
Response = c(
"count",
"count",
"count",
"count2",
"count2",
"count2",
"count",
"count",
"count",
"count",
"count",
"count2",
"count2",
"count2",
"count2",
"count2",
"count",
"count",
"count2",
"count2",
"count",
"count",
"count",
"count",
"count",
"count2",
"count2",
"count2",
"count2",
"count2"
),
Cleaned_Parameter = c(
"(Intercept)",
"child",
"camper",
"(Intercept)",
"child",
"livebait",
"persons.1",
"persons.2",
"persons.3",
"persons.4",
"count_Intercept",
"persons2.1",
"persons2.2",
"persons2.3",
"persons2.4",
"count2_Intercept",
"(Intercept)",
"camper",
"(Intercept)",
"child",
"persons.1",
"persons.2",
"persons.3",
"persons.4",
"zi_count_Intercept",
"persons2.1",
"persons2.2",
"persons2.3",
"persons2.4",
"zi_count2_Intercept"
)
),
class = c("clean_parameters", "data.frame"),
row.names = c(NA, -30L)
),
ignore_attr = TRUE
)
})
}
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