Nothing
test_that(".add_group_codings returns numerics and NAs.", {
facLabels <- c("a","b","c","d","e")
gs <- list(name = "subject", data = as.factor(facLabels),
numeric = as.numeric(as.factor(facLabels)),
K = 5, map = data.frame(numeric = as.numeric(as.factor(facLabels)),
label = as.factor(facLabels)))
newdata <- data.frame(subject = c("c", "f", NA),
A_1 = rnorm(3))
newdata <- .add_group_codings(gs, newdata)
expect_equal(newdata[1, "subject"], 3)
expect_equal(is.na(newdata[2, "subject"]), TRUE)
expect_equal(is.na(newdata[3, "subject"]), TRUE)
newdata <- data.frame(A_1 = rnorm(3))
newdata <- .add_group_codings(gs, newdata)
for(n in 1:nrow(newdata)) {
expect_equal(is.na(newdata[n, "subject"]), TRUE)
}
})
test_that("predict.lmmelsm is returning correct structures", {
library(LMMELSM)
data(sim_data)
new_data <- sim_data[c(1,51,101),]
iter <- 5
cores <- 1
chains <- 1
fit_int <- lmmelsm(list(A ~ A_1 + A_2 + A_3 + A_4 + A_5 + A_6,
N ~ N_1 + N_2 + N_3 + N_4 + N_5 + N_6
),
subject, sim_data, iter = iter, cores = cores, chains = chains)
pred <- predict(fit_int, new_data)
expect_equal(length(pred), 3)
expect_equal(nrow(pred$eta), 3 * 2)
expect_equal(nrow(pred$eta_logsd), 3 * 2)
expect_equal(nrow(pred$y), 3 * 12)
fit_obs <- lmmelsm(list(observed ~ A_1 + N_1), subject, sim_data, iter = iter, cores = cores, chains = chains)
pred <- predict(fit_obs, new_data)
expect_equal(nrow(pred$eta), nrow(pred$y))
expect_equal(nrow(pred$eta), 3 * 2)
expect_equal(nrow(pred$eta_logsd), 3 * 2)
expect_equal(nrow(pred$y), 3 * 2)
fit_obs_1 <- lmmelsm(observed ~ A_1, subject, sim_data, iter = iter, cores = cores, chains = chains)
pred <- predict(fit_obs_1, new_data)
expect_equal(nrow(pred$eta), nrow(pred$y))
expect_equal(nrow(pred$eta), 3 * 1)
expect_equal(nrow(pred$eta_logsd), 3 * 1)
expect_equal(nrow(pred$y), 3 * 1)
# Testing full gambit
fit_lat <- lmmelsm(list(A ~ A_1 + A_2 + A_3 + A_4 + A_5 + A_6,
N ~ N_1 + N_2 + N_3 + N_4 + N_5 + N_6,
location ~ x1 + x2 + baseline | x1 + x2,
scale ~ x1 + x2 + baseline | x1 + x2,
between ~ baseline),
subject, sim_data, iter = iter, cores = cores, chains = chains)
pred <- predict(fit_lat, new_data)
expect_equal(length(pred), 3)
expect_equal(nrow(pred$eta), 3 * 2)
expect_equal(nrow(pred$eta_logsd), 3 * 2)
expect_equal(nrow(pred$y), 3 * 12)
pred <- predict(fit_lat, new_data, summarize = FALSE)
expect_equal(length(pred), 3)
expect_equal(length(pred[[1]]), 3)
expect_equal(ncol(pred[[1]][["eta"]]), 2)
expect_equal(ncol(pred[[1]][["eta_logsd"]]), 2)
expect_equal(ncol(pred[[1]][["y"]]), 12)
})
test_that("fitted returns correct structures", {
library(LMMELSM)
data(sim_data)
iter <- 5
cores <- 1
chains <- 1
fit_lat <- lmmelsm(list(A ~ A_1 + A_2 + A_3 + A_4 + A_5 + A_6,
N ~ N_1 + N_2 + N_3 + N_4 + N_5 + N_6,
location ~ x1 + x2 + baseline | x1 + x2,
scale ~ x1 + x2 + baseline | x1 + x2,
between ~ baseline),
subject, sim_data, iter = iter, cores = cores, chains = chains)
fitted_lat <- fitted(fit_lat)
expect_equal(length(fitted_lat), 2)
expect_equal(nrow(fitted_lat[[1]]), nrow(sim_data) * 2)
expect_equal(nrow(fitted_lat[[2]]), nrow(sim_data) * 2)
})
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