Nothing
library(testthat)
library(lavaan)
library(semfindr)
mod <-
'
m1 ~ iv1 + c(a1, a2) * iv2
dv ~ c(b, b) * m1
a2b := a2 * b
'
dat <- pa_dat
dat0 <- dat[1:100, ]
set.seed(856041)
dat0$gp <- sample(c("gp2", "gp1"), size = nrow(dat0), replace = TRUE)
head(dat0)
fit0 <- lavaan::sem(mod, dat0, group = "gp")
fit0_data <- lav_data_used(fit0)
head(fit0_data)
fit0_free <- lavInspect(fit0, "free")
exo_vars <- lavNames(fit0, "ov.x")
fit0_data_exo <- dat0[, exo_vars]
fit0_data_exo_g <- split(fit0_data_exo, dat0$gp)
md_predictors_check <- lapply(fit0_data_exo_g,
function(x) {
mahalanobis(x, colMeans(x), cov(x))
})
md_predictors_check <- unlist(md_predictors_check)
j <- order(as.numeric(unlist((sapply(fit0_data_exo_g, rownames)))))
rerun_out <- lavaan_rerun(fit0, to_rerun = c(1, 3, 9, 15, 50), parallel = FALSE)
md_predictors_rerun <- mahalanobis_predictors(rerun_out)
test_that("Compare Mahalanobis distances: lavaan_rerun", {
expect_equal(ignore_attr = TRUE,
sort(as.vector(md_predictors_rerun)),
sort(md_predictors_check[j[c(1, 3, 9, 15, 50)]])
)
})
# Test whether an error will a vector of NAs will be returned if
# the fit object does not have exogenous observed variables.
mod <-
'
f1 =~ x1 + x2 + x3
f2 =~ x4 + x5 + x6
'
dat <- cfa_dat
dat0 <- dat[1:100, ]
dat0[1, 2] <- dat0[2, 3] <- dat0[3, 4] <- dat0[4, 1:6] <- NA
set.seed(15601)
dat0$gp <- sample(c("gp2", "gp1"), size = nrow(dat0), replace = TRUE)
head(dat0)
fit0 <- lavaan::cfa(mod, dat0, group = "gp", group.equal = "loadings")
rerun_out <- lavaan_rerun(fit0, parallel = FALSE, to_rerun = c(5, 8, 7, 9))
test_that("No exogenous observed variables", {
expect_warning(
md_predictors <- mahalanobis_predictors(fit0),
"The model has no exogenous observed variables."
)
expect_warning(
md_predictors_rerun <- mahalanobis_predictors(rerun_out),
"The model has no exogenous observed variables."
)
expect_true(all(is.na(md_predictors)))
expect_true(all(is.na(md_predictors_rerun)))
})
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