## ---- test-semmcci-mc-simple-med-std-defined-meanstructure lapply( X = 1, FUN = function(i, n, R, text) { message(text) seed <- 42 set.seed(seed) cp <- 0.00 b <- 0.10 a <- 0.10 sigma2ey <- 1 - b^2 - cp^2 - 2 * a * b * cp sigma2em <- 1 - a^2 sigma2x <- 1 coefs <- c( cp = cp, b = b, a = a, ab = a * b ) x <- rnorm(n = n, sd = sqrt(sigma2x)) m <- a * x + rnorm(n = n, sd = sqrt(sigma2em)) y <- cp * x + b * m + rnorm(n = n, sd = sqrt(sigma2ey)) data <- data.frame(x, m, y) model <- " y ~ cp * x + b * m m ~ a * x ab := a * b " fit <- lavaan::sem( data = data, model = model, fixed.x = FALSE, meanstructure = TRUE ) set.seed(seed) results_unstd_chol <- MC( fit, R = R, alpha = c(0.001, 0.01, 0.05), decomposition = "chol" ) results_unstd_chol$thetahatstar[3, ] <- lavaan::parameterEstimates(fit)$est results_chol <- MCStd(results_unstd_chol) set.seed(seed) results_unstd_eigen <- MC( fit, R = R, alpha = c(0.001, 0.01, 0.05), decomposition = "eigen" ) results_unstd_eigen$thetahatstar[3, ] <- lavaan::parameterEstimates(fit)$est results_eigen <- MCStd(results_unstd_eigen) set.seed(seed) results_unstd_svd <- MC( fit, R = R, alpha = c(0.001, 0.01, 0.05), decomposition = "svd" ) results_unstd_svd$thetahatstar[3, ] <- lavaan::parameterEstimates(fit)$est results_svd <- MCStd(results_unstd_svd) testthat::test_that( paste(text, "chol"), { testthat::expect_equal( .MCCI( results_chol )["ab", "97.5%"], quantile( results_chol$thetahatstar[, "ab"], .975, na.rm = TRUE ), check.attributes = FALSE ) } ) testthat::test_that( paste(text, "eigen"), { testthat::expect_equal( .MCCI( results_eigen )["ab", "97.5%"], quantile( results_eigen$thetahatstar[, "ab"], .975, na.rm = TRUE ), check.attributes = FALSE ) } ) testthat::test_that( paste(text, "svd"), { testthat::expect_equal( .MCCI( results_svd )["ab", "97.5%"], quantile( results_svd$thetahatstar[, "ab"], .975, na.rm = TRUE ), check.attributes = FALSE ) } ) }, n = 1000L, R = 2000L, text = "test-semmcci-mc-simple-med-std-defined-meanstructure" )