## ----include = FALSE---------------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) ## ----setup-------------------------------------------------------------------- library(semboottools) library(lavaan) ## ----eval = FALSE------------------------------------------------------------- # hist_qq_boot <- function(object, # param, # standardized = NULL, # nclass = NULL, # hist_color = "#5DADE233", # hist_linewidth = 1.5, # hist_border_color = "#1B4F72", # density_line_type = "solid", # density_line_color = "#8B0000CC", # density_line_linewidth = 2, # est_line_color = "#154360", # est_line_type = "dashed", # est_line_linewidth = 2, # qq_dot_pch = 21, # qq_dot_color = "#1B4F72", # qq_dot_fill = "#5DADE233", # qq_dot_size = 1.3, # qq_line_color = "#8B0000CC", # qq_line_linewidth = 2.1, # qq_line_linetype = "solid" # ) # # scatter_boot <- function(object, # params, # standardized = NULL, # main = "Bootstrap Estimates", # ) ## ----------------------------------------------------------------------------- library(lavaan) # Simulate data set.seed(1234) n <- 200 x <- runif(n) - 0.5 m <- 0.4 * x + rnorm(n) y <- 0.3 * m + rnorm(n) dat <- data.frame(x, m, y) # Specify model model <- ' m ~ a * x y ~ b * m + cp * x ab := a * b ' # Fit model fit1 <- sem(model, data = dat, se = "boot", bootstrap = 1000) fit2 <- sem(model, data = dat, fixed.x = FALSE) ## ----fig.width = 6, fig.height = 3,fig.align='center'------------------------- fit1 <- store_boot(fit1) hist_qq_boot(fit1, param = "ab", standardized = FALSE) ## ----------------------------------------------------------------------------- scatter_boot(fit1, c("ab", "a", "b"), standardized = FALSE) ## ----fig.width = 6, fig.height = 3, fig.align='center'------------------------ fit2 <- store_boot(fit2) hist_qq_boot(fit2, param = "ab", standardized = TRUE) ## ----------------------------------------------------------------------------- scatter_boot(fit2, c("a", "b", "ab"), standardized = TRUE)