library(tidySEM)
library(lavaan)
fit <- sem("mpg ~ a * am", data = mtcars)
tmp <- table_results(fit, columns = NULL)
test_that("table_results() returns all labels", {
expect_true(all(c("lavaan_label", "label") %in% names(tmp)))
expect_true(tmp$lavaan_label[1] == "a")
})
df <- HolzingerSwineford1939
names(df)[grepl("^x", names(df))] <- c("vis_1", "vis_2", "vis_3", "tex_1", "tex_2", "tex_3", "spe_1", "spe_2", "spe_3")
dict <- tidy_sem(df)
expect_true(all(dict$dictionary$scale[-c(1:6)] == rep(c("vis", "tex", "spe"), each = 3)))
measurement(dict, meanstructure = TRUE) -> model
res_lav <- sem(as_lavaan(model), data = df)
tb_lav <- table_results(res_lav, columns = NULL)
expect_true(nrow(tb_lav) == 36)
if(isTRUE(getOption("test_mplus"))){
test_that("table_results() returns all labels for mplus", {
the_test <- "mplus_labels"
old_wd <- getwd()
test_dir <- file.path(tempdir(), the_test)
dir.create(test_dir)
setwd(test_dir)
on.exit({unlink(test_dir, recursive = TRUE); setwd(old_wd)}, add = TRUE)
res_mplus <- mplusModeler(mplusObject(MODEL = "mpg ON am (a);", OUTPUT = "standardized;", rdata = mtcars), modelout = "test.inp", run = 1L)
tb_mplus <- table_results(res_mplus, columns = NULL)
expect_true("label" %in% names(tmp))
})
}
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