In marklhc/pinvsearch: Specification search for partial factorial invariance

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)

pinsearch

The goal of pinsearch is to automate the process of performing specification search in identifying noninvariant items to arrive at a partial factorial invariance model.

Installation

You can install the development version of pinsearch from GitHub with:

# install.packages("remotes")
remotes::install_github("marklhc/pinsearch")

Example

This is a basic example which shows you how to solve a common problem:

library(pinsearch)
library(lavaan)
HS.model <- '  visual =~ x1 + x2 + x3
              textual =~ x4 + x5 + x6
              speed   =~ x7 + x8 + x9 '
# Output the final partial invariance model, and the noninvariant items
pinSearch(HS.model, data = HolzingerSwineford1939, 
          group = "school", type = "intercepts")
# Compute dmacs effect size (added in version 0.1.2)
pinSearch(HS.model, data = HolzingerSwineford1939, 
          group = "school", type = "intercepts",
          effect_size = TRUE,
          progress = TRUE)

Example 2

A simulated example with ordinal data

# Simulate data
set.seed(2110)
library(MASS)
num_obs <- 500
lambda1 <- seq(.9, .6, length.out = 7)
lambda2 <- c(lambda1[1], 1, lambda1[3:7])
cov1 <- tcrossprod(lambda1) + diag(.5, 7)
dimnames(cov1) <- list(paste0("yy", 1:7), paste0("yy", 1:7))
thres1 <- rbind(seq(-1.5, 1.5, length.out = 7))
thres2 <- rbind(c(thres1[1], 0.25, thres1[3:6], 0.3))
mean1 <- rep(0, 7)
ystar1 <- mvrnorm(num_obs, mu = mean1, Sigma = cov1)
y1 <- ystar1
cov2 <- tcrossprod(lambda1) * 1.3 + diag(.5, 7)
dimnames(cov2) <- dimnames(cov1)
mean2 <- lambda1 * .4
ystar2 <- mvrnorm(num_obs, mu = mean2, Sigma = cov2)
y2 <- ystar2
# Ordinal indicators
thres1 <- rbind(seq(-1.5, 0, length.out = 7),
                seq(-0.5, 0.25, length.out = 7),
                rep(1, 7))
thres2 <- rbind(c(thres1[1, 1], -0.5, thres1[1, 3:6], -0.5),
                thres1[2,],
                c(rep(1, 3), rep(0.5, 2), rep(1, 2)))
for (j in seq_len(ncol(ystar1))) {
    y1[, j] <- findInterval(ystar1[, j], thres1[, j])
}
for (j in seq_len(ncol(ystar2))) {
    y2[, j] <- findInterval(ystar2[, j], thres2[, j])
}
df <- rbind(cbind(y1, group = 1), cbind(y2, group = 2))

pinSearch(' f =~ yy1 + yy2 + yy3 + yy4 + yy5 + yy6 + yy7 ',
          data = df, group = "group", type = "thresholds",
          ordered = paste0("yy", 1:7),
          effect_size = TRUE)