Nothing
suppressWarnings(library(lavaan))
latent <- list(
visual = paste0("x", 1:3),
textual = paste0("x", 4:6),
speed = paste0("x", 7:9)
)
regression <- list(
ageyr = c("visual", "textual", "speed"),
grade = c("visual", "textual", "speed")
)
HS.model <- write_lavaan(latent = latent, regression = regression)
fit <- sem(HS.model, data = HolzingerSwineford1939)
data_labels <- data.frame(
x1 = "I have good visual perception",
x2 = "I have good cube perception",
x3 = "I have good at lozenge perception",
x4 = "I have paragraph comprehension",
x5 = "I am good at sentence completion",
x6 = "I excel at finding the meaning of words",
x7 = "I am quick at doing mental additions",
x8 = "I am quick at counting dots",
x9 = "I am quick at discriminating straight and curved capitals"
)
# ____________________________________________________________________________
# Tests ####
test_that("nice_modindices regular", {
skip_if_not_installed("sjlabelled")
expect_snapshot(
nice_modindices(fit, maximum.number = 5)
)
})
test_that("nice_modindices labels", {
skip_if_not_installed("stringdist")
expect_snapshot(
nice_modindices(
fit,
maximum.number = 10,
labels = data_labels, op = "~~"
)
)
})
test_that("nice_modindices auto-labels", {
skip_if_not_installed("sjlabelled")
skip_if_not_installed("stringdist")
x <- HolzingerSwineford1939
x <- sjlabelled::set_label(x, label = paste0("I am ", seq_len(ncol(x)), " years old."))
fit <- sem(HS.model, data = x)
expect_snapshot(
nice_modindices(
fit,
maximum.number = 10,
op = "~~"
)
)
})
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