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test_that("n_factors, default", {
skip_if_not_installed("nFactors")
skip_if_not_installed("psych")
set.seed(333)
x <- n_factors(mtcars[, 1:4])
expect_identical(ncol(x), 3L)
})
test_that("n_factors, EGAnet", {
skip_on_cran()
skip_if_not_installed("EGAnet")
set.seed(333)
x <- n_factors(mtcars, package = "EGAnet")
expect_identical(ncol(x), 3L)
expect_identical(
print(capture.output(x)),
c(
"# Method Agreement Procedure:",
"",
"The choice of 3 dimensions is supported by 2 (100.00%) methods out of 2 (EGA (glasso), EGA (TMFG))."
)
)
})
test_that("n_factors, EGAnet does not fail", {
skip_on_cran()
skip_if_not_installed("EGAnet")
set.seed(333)
x <- n_factors(mtcars[, 1:4], package = "EGAnet")
expect_identical(ncol(x), 3L)
expect_identical(nrow(x), 1L)
expect_identical(
print(capture.output(x)),
c(
"# Method Agreement Procedure:",
"",
"The choice of 1 dimensions is supported by 1 (100.00%) methods out of 1 (EGA (glasso))."
)
)
})
test_that("n_factors, oblimin rotation", {
skip_if_not_installed("nFactors")
skip_if_not_installed("psych")
skip_if_not_installed("GPArotation")
set.seed(333)
x <- n_factors(mtcars[, 1:4], type = "PCA", rotation = "oblimin")
expect_identical(ncol(x), 3L)
expect_identical(
print(capture.output(x)),
c(
"# Method Agreement Procedure:",
"",
"The choice of 1 dimensions is supported by 11 (84.62%) methods out of 13 (Bartlett, Anderson, Lawley, Optimal coordinates, Acceleration factor, Parallel analysis, Kaiser criterion, Scree (SE), Scree (R2), VSS complexity 1, Velicer's MAP)." # nolint
)
)
})
test_that("n_factors, no rotation, psych only", {
skip_if_not_installed("nFactors")
skip_if_not_installed("psych")
set.seed(333)
x <- n_factors(mtcars[, 1:4], rotation = "none", package = "psych")
expect_identical(ncol(x), 3L)
expect_identical(
print(capture.output(x)),
c(
"# Method Agreement Procedure:",
"",
"The choice of 1 dimensions is supported by 3 (60.00%) methods out of 5 (Velicer's MAP, BIC, BIC (adjusted))."
)
)
})
test_that("n_factors, variance explained", {
skip_on_cran()
skip_if_not_installed("nFactors")
skip_if_not_installed("psych")
set.seed(333)
x <- n_factors(mtcars[, 1:4], type = "PCA")
expect_equal(
attributes(x)$Variance_Explained$Variance_Cumulative,
c(0.84126, 0.85088, 0.85859, 0.85859),
tolerance = 1e-4
)
})
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