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test_that("principal_components", {
skip_if_not_installed("psych")
set.seed(333)
x <- principal_components(mtcars[, 1:7], n = "all", threshold = 0.2)
expect_identical(c(ncol(x), nrow(x)), c(8L, 7L))
x <- suppressMessages(principal_components(
mtcars[, 1:7],
n = 2,
rotation = "oblimin",
threshold = "max",
sort = TRUE
))
expect_identical(c(ncol(x), nrow(x)), c(6L, 7L))
pca <- principal_components(mtcars[, 1:5], n = 2)
expect_identical(c(ncol(pca), nrow(pca)), c(4L, 5L))
x <- summary(pca)
expect_identical(c(ncol(x), nrow(x)), c(3L, 4L))
x <- model_parameters(pca)
expect_identical(c(ncol(x), nrow(x)), c(5L, 2L))
x <- predict(pca)
expect_identical(c(ncol(x), nrow(x)), c(2L, 32L))
})
test_that("efa-cfa", {
skip_if_not_installed("psych")
skip_if_not_installed("lavaan")
efa <- psych::fa(attitude, nfactors = 3)
params <- parameters::model_parameters(efa)
expect_identical(c(nrow(params), ncol(params)), c(7L, 6L))
model1 <- efa_to_cfa(efa)
model2 <- efa_to_cfa(efa, threshold = 0.3)
expect_identical(nchar(model1), 109L)
m1 <- suppressWarnings(lavaan::cfa(model1, data = attitude))
params <- parameters::model_parameters(m1)
expect_identical(c(nrow(params), ncol(params)), c(10L, 10L))
expect_message(parameters::model_parameters(m1, ci = c(0.8, 0.9)))
params <- parameters::model_parameters(m1, standardize = TRUE, component = "all")
expect_identical(c(nrow(params), ncol(params)), c(20L, 10L))
x <- lavaan::anova(m1, lavaan::cfa(model2, data = attitude))
params <- parameters::model_parameters(x)
expect_identical(c(nrow(params), ncol(params)), c(2L, 6L))
})
test_that("FactoMineR", {
skip_if_not_installed("FactoMineR")
x <- suppressWarnings(model_parameters(
FactoMineR::PCA(mtcars, ncp = 3, graph = FALSE),
threshold = 0.2,
sort = TRUE
))
expect_identical(c(ncol(x), nrow(x)), c(5L, 11L))
# x <- suppressWarnings(model_parameters(FactoMineR::FAMD(iris, ncp = 3, graph = FALSE), threshold = 0.2, sort = TRUE))
# expect_identical(c(ncol(x), nrow(x)), c(5L, 5L))
})
test_that("BayesFM", {
skip_if_not_installed("BayesFM")
set.seed(333)
befa <- BayesFM::befa(mtcars, iter = 1000, verbose = FALSE)
params <- suppressWarnings(parameters::model_parameters(befa, sort = TRUE))
expect_identical(nrow(params), 11L)
})
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