.runThisTest <- Sys.getenv("RunAllinsightTests") == "yes"
if (.runThisTest) {
unloadNamespace("gam")
if (requiet("mgcv")) {
set.seed(0)
void <- capture.output(dat <- gamSim(6, n = 200, scale = 0.2, dist = "poisson"))
m1 <-
gamm(
y ~ s(x0) + s(x1) + s(x2),
family = poisson,
data = dat,
random = list(fac = ~1),
verbosePQL = FALSE
)
test_that("model_info", {
expect_true(model_info(m1)$is_poisson)
expect_false(model_info(m1)$is_linear)
})
test_that("clean_names", {
expect_equal(clean_names(m1), c("y", "x0", "x1", "x2", "fac"))
})
test_that("find_predictors", {
expect_identical(find_predictors(m1), list(conditional = c("x0", "x1", "x2")))
expect_identical(
find_predictors(m1, effects = "all"),
list(
conditional = c("x0", "x1", "x2"),
random = "fac"
)
)
expect_identical(find_predictors(m1, flatten = TRUE), c("x0", "x1", "x2"))
expect_identical(find_predictors(m1, effects = "random"), list(random = "fac"))
})
test_that("find_response", {
expect_identical(find_response(m1), "y")
})
test_that("get_response", {
expect_equal(get_response(m1), dat$y)
})
test_that("link_inverse", {
expect_equal(link_inverse(m1)(0.2), exp(0.2), tolerance = 1e-5)
})
test_that("get_data", {
expect_equal(nrow(get_data(m1)), 200)
expect_equal(colnames(get_data(m1)), c("y", "x0", "x1", "x2", "fac", "g", "g.0", "g.1", "y.0", "Xr.V1", "Xr.V2", "Xr.V3", "Xr.V4", "Xr.V5", "Xr.V6", "Xr.V7", "Xr.V8", "Xr.0.V1", "Xr.0.V2", "Xr.0.V3", "Xr.0.V4", "Xr.0.V5", "Xr.0.V6", "Xr.0.V7", "Xr.0.V8", "Xr.1.V1", "Xr.1.V2", "Xr.1.V3", "Xr.1.V4", "Xr.1.V5", "Xr.1.V6", "Xr.1.V7", "Xr.1.V8", "X.(Intercept)", "X.s(x0)Fx1", "X.s(x1)Fx1", "X.s(x2)Fx1"))
})
test_that("find_formula", {
expect_length(find_formula(m1), 2)
expect_equal(
find_formula(m1),
list(
conditional = as.formula("y ~ s(x0) + s(x1) + s(x2)"),
random = as.formula("~1 | fac")
),
ignore_attr = TRUE
)
})
test_that("find_terms", {
expect_equal(find_terms(m1), list(response = "y", conditional = c("s(x0)", "s(x1)", "s(x2)"), random = "fac"))
expect_equal(find_terms(m1, flatten = TRUE), c("y", "s(x0)", "s(x1)", "s(x2)", "fac"))
})
test_that("find_variables", {
expect_equal(find_variables(m1), list(response = "y", conditional = c("x0", "x1", "x2"), random = "fac"))
expect_equal(find_variables(m1, flatten = TRUE), c("y", "x0", "x1", "x2", "fac"))
})
test_that("n_obs", {
expect_equal(n_obs(m1), 200)
})
test_that("linkfun", {
expect_false(is.null(link_function(m1)))
})
test_that("find_parameters", {
expect_equal(
find_parameters(m1),
list(
conditional = "(Intercept)",
smooth_terms = c("s(x0)", "s(x1)", "s(x2)")
)
)
expect_equal(nrow(get_parameters(m1)), 4)
expect_equal(get_parameters(m1)$Parameter, c("(Intercept)", "s(x0)", "s(x1)", "s(x2)"))
})
test_that("is_multivariate", {
expect_false(is_multivariate(m1))
})
# test formula random effects -----------------------
n <- 200
sig <- 2
set.seed(0)
n.g <- 10
n <- n.g * 10 * 4
void <- capture.output(dat <- gamSim(1, n = n, scale = 2))
f <- dat$f
## simulate nested random effects....
fa <- as.factor(rep(1:10, rep(4 * n.g, 10)))
ra <- rep(rnorm(10), rep(4 * n.g, 10))
fb <- as.factor(rep(rep(1:4, rep(n.g, 4)), 10))
rb <- rep(rnorm(4), rep(n.g, 4))
for (i in 1:9) {
rb <- c(rb, rep(rnorm(4), rep(n.g, 4)))
}
## simulate auto-correlated errors within groups
e <- array(0, 0)
for (i in 1:40) {
eg <- rnorm(n.g, 0, sig)
for (j in 2:n.g) {
eg[j] <- eg[j - 1] * 0.6 + eg[j]
}
e <- c(e, eg)
}
dat$y <- f + ra + rb + e
dat$fa <- fa
dat$fb <- fb
## fit model ....
m1 <- gamm(
y ~ s(x0, bs = "cr") + s(x1, bs = "cr"),
data = dat,
random = list(fa = ~1, fb = ~1),
correlation = corAR1()
)
set.seed(0)
void <- capture.output(
dat <- gamSim(6, n = 200, scale = 0.2, dist = "poisson")
)
m2 <- gamm(
y ~ s(x0) + s(x1) + s(x2),
family = poisson,
data = dat,
verbosePQL = FALSE
)
dat$g <- dat$fac
m3 <- gamm(
y ~ s(x0) + s(x1) + s(x2),
family = poisson,
data = dat,
random = list(g = ~1),
verbosePQL = FALSE
)
test_that("find_formula-gamm-1", {
expect_equal(
find_formula(m1),
list(
conditional = as.formula("y ~ s(x0, bs = \"cr\") + s(x1, bs = \"cr\")"),
random = list(as.formula("~1 | fa"), as.formula("~1 | fb"))
),
ignore_attr = TRUE
)
})
test_that("find_formula-gamm-2", {
expect_equal(
find_formula(m2),
list(conditional = as.formula("y ~ s(x0) + s(x1) + s(x2)")),
ignore_attr = TRUE
)
})
test_that("find_formula-gamm-3", {
expect_equal(
find_formula(m3),
list(
conditional = as.formula("y ~ s(x0) + s(x1) + s(x2)"),
random = as.formula("~1 | g")
),
ignore_attr = TRUE
)
})
}
}
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