.runThisTest <- Sys.getenv("RunAllinsightTests") == "yes"
.runStanTest <- Sys.getenv("RunAllinsightStanTests") == "yes"
if (.runThisTest && .runStanTest) {
if (suppressWarnings(
requiet("lme4") &&
requiet("BayesFactor") &&
requiet("rstanarm")
)) {
# skip_on_cran()
skip_if_offline()
# defining models ---------------------
m1 <- insight::download_model("stanreg_merMod_5")
m2 <- insight::download_model("stanreg_glm_6")
m3 <- insight::download_model("stanreg_glm_1")
data("puzzles")
m4 <- suppressWarnings(
stan_glm(
RT ~ color * shape,
data = puzzles,
prior = rstanarm::cauchy(0, c(3, 1, 2)),
iter = 500,
chains = 2,
refresh = 0
)
)
m5 <- suppressWarnings(
stan_glm(
RT ~ color * shape,
data = puzzles,
prior = rstanarm::cauchy(0, c(1, 2, 3)),
iter = 500,
chains = 2,
refresh = 0
)
)
m6 <- insight::download_model("stanreg_gamm4_1")
m7 <- suppressWarnings(
stan_lm(mpg ~ wt + qsec + am,
data = mtcars, prior = R2(0.75),
chains = 1, iter = 300, refresh = 0
)
)
m8 <- stan_lmer(Reaction ~ Days + (1 | Subject), data = sleepstudy, refresh = 0)
m9 <- stan_aov(yield ~ block + N * P * K, data = npk, prior = R2(0.5), refresh = 0)
N <- 200
x <- rnorm(N, 2, 1)
z <- rnorm(N, 2, 1)
mu <- binomial(link = "logit")$linkinv(1 + 0.2 * x)
phi <- exp(1.5 + 0.4 * z)
y <- rbeta(N, mu * phi, (1 - mu) * phi)
hist(y, col = "dark grey", border = FALSE, xlim = c(0, 1))
fake_dat <- data.frame(y, x, z)
m10 <- stan_betareg(
y ~ x | z,
data = fake_dat,
link = "logit",
link.phi = "log",
refresh = 0,
algorithm = "optimizing" # just for speed of example
)
ols <- lm(mpg ~ wt + qsec + am,
data = mtcars, # all row are complete so ...
na.action = na.exclude
) # not necessary in this case
b <- coef(ols)[-1]
R <- qr.R(ols$qr)[-1, -1]
SSR <- crossprod(ols$residuals)[1]
not_NA <- !is.na(fitted(ols))
N <- sum(not_NA)
xbar <- colMeans(mtcars[not_NA, c("wt", "qsec", "am")])
y <- mtcars$mpg[not_NA]
ybar <- mean(y)
s_y <- sd(y)
m11 <- suppressWarnings(
stan_biglm.fit(b, R, SSR, N, xbar, ybar, s_y,
prior = R2(0.75),
# the next line is only to make the example go fast
refresh = 0,
chains = 1, iter = 500, seed = 12345
)
)
dat <- infert[order(infert$stratum), ] # order by strata
m12 <- suppressWarnings(
stan_clogit(case ~ spontaneous + induced + (1 | education),
strata = stratum,
data = dat,
subset = parity <= 2,
QR = TRUE,
chains = 2, iter = 500, refresh = 0
)
) # for speed only
if (.Platform$OS.type != "windows" || .Platform$r_arch != "i386") {
void <- capture.output(
m13 <- suppressMessages(
suppressWarnings(
stan_jm(
formulaLong = logBili ~ year + (1 | id),
dataLong = pbcLong,
formulaEvent = Surv(futimeYears, death) ~ sex + trt,
dataEvent = pbcSurv,
time_var = "year",
# this next line is only to keep the example small in size!
chains = 1, cores = 1, seed = 12345, iter = 1000, refresh = 0
)
)
)
)
# expect_snapshot(model_info(m13))
}
data("Orange", package = "datasets")
Orange$circumference <- Orange$circumference / 100
Orange$age <- Orange$age / 100
## TODO probably re-enable once strange check error is resolved
# m14 <- stan_nlmer(
# circumference ~ SSlogis(age, Asym, xmid, scal) ~ Asym | Tree,
# data = Orange,
# # for speed only
# chains = 1,
# iter = 1000
# )
m15 <- suppressWarnings(
stan_mvmer(
formula = list(
logBili ~ year + (1 | id),
albumin ~ sex + year + (year | id)
),
data = pbcLong,
# this next line is only to keep the example small in size!
chains = 1, cores = 1, seed = 12345, iter = 1000, refresh = 0
)
)
test_that("model_info-stanreg-glm", {
expect_equal(
model_info(m1),
list(
is_binomial = TRUE, is_bernoulli = FALSE, is_count = FALSE,
is_poisson = FALSE, is_negbin = FALSE, is_beta = FALSE, is_betabinomial = FALSE,
is_dirichlet = FALSE, is_exponential = FALSE, is_logit = TRUE,
is_probit = FALSE, is_censored = FALSE, is_truncated = FALSE,
is_survival = FALSE, is_linear = FALSE, is_tweedie = FALSE,
is_zeroinf = FALSE, is_zero_inflated = FALSE, is_dispersion = FALSE,
is_hurdle = FALSE, is_ordinal = FALSE, is_cumulative = FALSE,
is_multinomial = FALSE, is_categorical = FALSE, is_mixed = TRUE,
is_multivariate = FALSE, is_trial = TRUE, is_bayesian = TRUE,
is_gam = FALSE, is_anova = FALSE, is_timeseries = FALSE,
is_ttest = FALSE, is_correlation = FALSE, is_onewaytest = FALSE,
is_chi2test = FALSE, is_ranktest = FALSE, is_levenetest = FALSE,
is_variancetest = FALSE, is_xtab = FALSE, is_proptest = FALSE,
is_binomtest = FALSE, is_ftest = FALSE, is_meta = FALSE,
link_function = "logit", family = "binomial", n_obs = 56L,
n_grouplevels = c(herd = 15L)
)
)
expect_equal(
model_info(m2),
list(
is_binomial = FALSE, is_bernoulli = FALSE, is_count = FALSE,
is_poisson = FALSE, is_negbin = FALSE, is_beta = FALSE, is_betabinomial = FALSE,
is_dirichlet = FALSE, is_exponential = FALSE, is_logit = FALSE,
is_probit = FALSE, is_censored = FALSE, is_truncated = FALSE,
is_survival = FALSE, is_linear = TRUE, is_tweedie = FALSE,
is_zeroinf = FALSE, is_zero_inflated = FALSE, is_dispersion = FALSE,
is_hurdle = FALSE, is_ordinal = FALSE, is_cumulative = FALSE,
is_multinomial = FALSE, is_categorical = FALSE, is_mixed = FALSE,
is_multivariate = FALSE, is_trial = FALSE, is_bayesian = TRUE,
is_gam = FALSE, is_anova = FALSE, is_timeseries = FALSE,
is_ttest = FALSE, is_correlation = FALSE, is_onewaytest = FALSE,
is_chi2test = FALSE, is_ranktest = FALSE, is_levenetest = FALSE,
is_variancetest = FALSE, is_xtab = FALSE, is_proptest = FALSE,
is_binomtest = FALSE, is_ftest = FALSE, is_meta = FALSE,
link_function = "identity", family = "gaussian", n_obs = 150L,
n_grouplevels = NULL
)
)
expect_equal(
model_info(m3),
list(
is_binomial = TRUE, is_bernoulli = TRUE, is_count = FALSE,
is_poisson = FALSE, is_negbin = FALSE, is_beta = FALSE, is_betabinomial = FALSE,
is_dirichlet = FALSE, is_exponential = FALSE, is_logit = TRUE,
is_probit = FALSE, is_censored = FALSE, is_truncated = FALSE,
is_survival = FALSE, is_linear = FALSE, is_tweedie = FALSE,
is_zeroinf = FALSE, is_zero_inflated = FALSE, is_dispersion = FALSE,
is_hurdle = FALSE, is_ordinal = FALSE, is_cumulative = FALSE,
is_multinomial = FALSE, is_categorical = FALSE, is_mixed = FALSE,
is_multivariate = FALSE, is_trial = FALSE, is_bayesian = TRUE,
is_gam = FALSE, is_anova = FALSE, is_timeseries = FALSE,
is_ttest = FALSE, is_correlation = FALSE, is_onewaytest = FALSE,
is_chi2test = FALSE, is_ranktest = FALSE, is_levenetest = FALSE,
is_variancetest = FALSE, is_xtab = FALSE, is_proptest = FALSE,
is_binomtest = FALSE, is_ftest = FALSE, is_meta = FALSE,
link_function = "logit", family = "binomial", n_obs = 32L,
n_grouplevels = NULL
)
)
## TODO add model m4 to m15
})
test_that("n_parameters", {
expect_equal(n_parameters(m1), 21)
expect_equal(n_parameters(m1, effects = "fixed"), 5)
})
test_that("get_priors", {
expect_equal(
colnames(get_priors(m1)),
c("Parameter", "Distribution", "Location", "Scale")
)
expect_equal(
colnames(get_priors(m2)),
c(
"Parameter",
"Distribution",
"Location",
"Scale",
"Adjusted_Scale"
)
)
expect_equal(get_priors(m1)$Scale, c(2.5, 2.5, 2.5, 2.5, 2.5), tolerance = 1e-3)
expect_equal(get_priors(m2)$Adjusted_Scale, c(1.08967, 2.30381, 2.30381, 0.61727, 0.53603, 0.41197), tolerance = 1e-3)
expect_equal(get_priors(m3)$Adjusted_Scale, c(NA, 2.555042), tolerance = 1e-3)
expect_equal(get_priors(m4)$Adjusted_Scale, c(6.399801, NA, NA, NA), tolerance = 1e-3)
expect_equal(get_priors(m5)$Adjusted_Scale, c(6.399801, NA, NA, NA), tolerance = 1e-3)
expect_equal(
get_priors(m6),
data.frame(
Parameter = "(Intercept)",
Distribution = "normal",
Location = 3.057333,
Scale = 2.5,
Adjusted_Scale = 1.089666,
stringsAsFactors = FALSE,
row.names = NULL
),
tolerance = 1e-3
)
})
test_that("clean_names", {
expect_identical(
clean_names(m1),
c("incidence", "size", "period", "herd")
)
})
test_that("find_predictors", {
expect_identical(find_predictors(m1), list(conditional = c("size", "period")))
expect_identical(find_predictors(m1, flatten = TRUE), c("size", "period"))
expect_identical(
find_predictors(m1, effects = "all", component = "all"),
list(
conditional = c("size", "period"),
random = "herd"
)
)
expect_identical(
find_predictors(
m1,
effects = "all",
component = "all",
flatten = TRUE
),
c("size", "period", "herd")
)
})
test_that("find_response", {
expect_equal(
find_response(m1, combine = TRUE),
"cbind(incidence, size - incidence)"
)
expect_equal(
find_response(m1, combine = FALSE),
c("incidence", "size")
)
})
test_that("get_response", {
expect_equal(nrow(get_response(m1)), 56)
expect_equal(colnames(get_response(m1)), c("incidence", "size"))
})
test_that("find_random", {
expect_equal(find_random(m1), list(random = "herd"))
})
test_that("get_random", {
expect_equal(get_random(m1), lme4::cbpp[, "herd", drop = FALSE])
})
test_that("find_terms", {
expect_identical(
find_terms(m1),
list(
response = "cbind(incidence, size - incidence)",
conditional = c("size", "period"),
random = "herd"
)
)
})
test_that("find_variables", {
expect_identical(
find_variables(m1),
list(
response = c("incidence", "size"),
conditional = c("size", "period"),
random = "herd"
)
)
expect_identical(
find_variables(m1, effects = "fixed"),
list(
response = c("incidence", "size"),
conditional = c("size", "period")
)
)
expect_null(find_variables(m1, component = "zi"))
})
test_that("n_obs", {
expect_equal(n_obs(m1), 56)
expect_equal(n_obs(m1, disaggregate = TRUE), 842)
})
test_that("find_paramaters", {
expect_equal(
find_parameters(m1),
list(
conditional = c("(Intercept)", "size", "period2", "period3", "period4"),
random = c(sprintf("b[(Intercept) herd:%i]", 1:15), "Sigma[herd:(Intercept),(Intercept)]")
)
)
expect_equal(
find_parameters(m1, flatten = TRUE),
c(
"(Intercept)",
"size",
"period2",
"period3",
"period4",
sprintf("b[(Intercept) herd:%i]", 1:15),
"Sigma[herd:(Intercept),(Intercept)]"
)
)
})
test_that("find_paramaters", {
expect_equal(
colnames(get_parameters(m1)),
c("(Intercept)", "size", "period2", "period3", "period4")
)
expect_equal(
colnames(get_parameters(m1, effects = "all")),
c(
"(Intercept)",
"size",
"period2",
"period3",
"period4",
sprintf("b[(Intercept) herd:%i]", 1:15),
"Sigma[herd:(Intercept),(Intercept)]"
)
)
})
test_that("linkfun", {
expect_false(is.null(link_function(m1)))
})
test_that("link_inverse", {
expect_equal(link_inverse(m1)(0.2), plogis(0.2), tolerance = 1e-4)
})
test_that("get_data", {
expect_equal(nrow(get_data(m1)), 56)
expect_equal(
colnames(get_data(m1)),
c("incidence", "size", "period", "herd")
)
})
test_that("find_formula", {
expect_length(find_formula(m1), 2)
expect_equal(
find_formula(m1),
list(
conditional = as.formula("cbind(incidence, size - incidence) ~ size + period"),
random = as.formula("~1 | herd")
),
ignore_attr = TRUE
)
})
test_that("get_variance", {
expect_equal(
get_variance(m1),
list(
var.fixed = 0.36274,
var.random = 0.5988885,
var.residual = 3.28987,
var.distribution = 3.28987,
var.dispersion = 0,
var.intercept = c(herd = 0.59889)
),
tolerance = 1e-3
)
expect_equal(get_variance_fixed(m1),
c(var.fixed = 0.3627389),
tolerance = 1e-4
)
expect_equal(get_variance_random(m1),
c(var.random = 0.5988885),
tolerance = 1e-4
)
expect_equal(get_variance_residual(m1),
c(var.residual = 3.289868),
tolerance = 1e-4
)
expect_equal(get_variance_distribution(m1),
c(var.distribution = 3.289868),
tolerance = 1e-4
)
expect_equal(get_variance_dispersion(m1),
c(var.dispersion = 0),
tolerance = 1e-4
)
})
test_that("find_algorithm", {
expect_equal(
find_algorithm(m1),
list(
algorithm = "sampling",
chains = 2,
iterations = 500,
warmup = 250
)
)
})
test_that("clean_parameters", {
expect_equal(
clean_parameters(m2),
structure(
list(
Parameter = c(
"(Intercept)",
"Speciesversicolor",
"Speciesvirginica",
"Petal.Length",
"Speciesversicolor:Petal.Length",
"Speciesvirginica:Petal.Length",
"sigma"
),
Effects = c(
"fixed", "fixed", "fixed",
"fixed", "fixed", "fixed", "fixed"
),
Component = c(
"conditional",
"conditional",
"conditional",
"conditional",
"conditional",
"conditional",
"sigma"
),
Cleaned_Parameter = c(
"(Intercept)",
"Speciesversicolor",
"Speciesvirginica",
"Petal.Length",
"Speciesversicolor:Petal.Length",
"Speciesvirginica:Petal.Length",
"sigma"
)
),
class = c("clean_parameters", "data.frame"),
row.names = c(NA, -7L)
),
ignore_attr = TRUE
)
})
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))
expect_null(find_statistic(m6))
})
model <- stan_glm(
disp ~ carb,
data = mtcars,
priors = NULL,
prior_intercept = NULL,
refresh = 0
)
test_that("flat_priors", {
p <- get_priors(model)
expect_equal(p$Distribution, c("uniform", "normal"))
expect_equal(p$Location, c(NA, 0), tolerance = 1e-3)
})
}
}
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