#### CFA measurement ####
test_that("makeTable creates propper tibble for model = cfa and tabletyle = measurement", {
dvn <- scrapeVarCross(dat = commitmentM, x_order = "sip", x_stem = "sat.g",
x_delim2="_", distinguish_1="f", distinguish_2="m")
script <- scriptCor(dvn, lvname = "Sat",
constr_dy_meas = c("loadings", "intercepts", "residuals"),
constr_dy_struct = "none")
fit <- lavaan::cfa(script, data = commitmentM, std.lv = FALSE,
auto.fix.first= FALSE, meanstructure = TRUE)
expect_equal(dySEM:::makeTable(dvn, fit, model = "cfa", tabletype = "measurement", gtTab = FALSE),
structure(list(`Latent Factor` = c("Satf", "Satf", "Satf", "Satf",
"Satf", "Satm", "Satm", "Satm", "Satm", "Satm"), Indicator = c("sat.g1_f",
"sat.g2_f", "sat.g3_f", "sat.g4_f", "sat.g5_f", "sat.g1_m", "sat.g2_m",
"sat.g3_m", "sat.g4_m", "sat.g5_m"), Loading = c(1.929, 1.742,
2.087, 1.985, 2.082, 1.929, 1.742, 2.087, 1.985, 2.082), SE = c(0.089,
0.093, 0.096, 0.089, 0.098, 0.089, 0.093, 0.096, 0.089, 0.098
), Z = c(21.725, 18.699, 21.67, 22.281, 21.301, 21.725, 18.699,
21.67, 22.281, 21.301), `p-value` = c("< .001", "< .001", "< .001",
"< .001", "< .001", "< .001", "< .001", "< .001", "< .001", "< .001"
), `Std. Loading` = c(0.939, 0.829, 0.941, 0.963, 0.925, 0.939,
0.828, 0.941, 0.963, 0.924), Intercept = c(7.454, 7.19, 7.031,
7.47, 7.201, 7.454, 7.19, 7.031, 7.47, 7.201)), class = c("tbl_df",
"tbl", "data.frame"), row.names = c(NA, -10L))
)
})
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