tspa_mx_model: Two-Stage Path Analysis with Definition Variables Using...

In Gengrui-Zhang/R2spa: An R package for two-stage path analysis (2S-PA) to adjust for measurement errors

Two-Stage Path Analysis with Definition Variables Using OpenMx

Description

Fit a two-stage path analysis (2S-PA) model.

Usage

tspa_mx_model(
  mx_model,
  data,
  mat_ld,
  mat_ev,
  mat_int = NULL,
  fs_lv_names = NULL,
  ...
)

Arguments

mx_model	A model object of class OpenMx::MxRAMModel, created by OpenMx::mxModel() or the umx package. This is the structural model part.
data	A data frame containing factor scores.
mat_ld	A p \times p matrix indicating the loadings of the factor scores on the latent variables. The ith row indicate loadings of the ith factor score variable on the latent variables. This could be one of the following: A matrix created by OpenMx::mxMatrix() with loading values. A named numeric matrix, with rownames and column names matching the factor score and latent variables. A named character matrix, with rownames and column names matching the factor score and latent variables, and the character values indicating the variable names in data for the corresponding loadings.
mat_ev	Similar to mat_ld but for the error variance-covariance matrix of the factor scores.
mat_int	Similar to mat_ld but for the measurement intercept matrix of the factor scores.
fs_lv_names	A named character vector where each element is the name of a factor score variable, and the names of the vector are the corresponding name of the latent variables.
...	Additional arguments passed to OpenMx::mxModel().

Value

An object of class OpenMx::MxModel. Note that the model has not been run.

Examples

library(mirt)
library(umx)
library(OpenMx)
# Simulate data with mirt
set.seed(1324)
num_obs <- 100
# Simulate theta
eta <- MASS::mvrnorm(num_obs, mu = c(0, 0), Sigma = diag(c(1, 1 - 0.5^2)),
                     empirical = TRUE)
th1 <- eta[, 1]
th2 <- -1 + 0.5 * th1 + eta[, 2]
# items and response data
a1 <- matrix(1, 10)
d1 <- matrix(rnorm(10))
a2 <- matrix(runif(10, min = 0.5, max = 1.5))
d2 <- matrix(rnorm(10))
dat1 <- mirt::simdata(a = a1, d = d1,
                      N = num_obs, itemtype = "2PL", Theta = th1)
dat2 <- mirt::simdata(a = a2, d = d2, N = num_obs,
                      itemtype = "2PL", Theta = th2)
# Factor scores
mod1 <- mirt(dat1, model = 1, itemtype = "Rasch", verbose = FALSE)
mod2 <- mirt(dat2, model = 1, itemtype = "2PL", verbose = FALSE)
fs1 <- fscores(mod1, full.scores.SE = TRUE)
fs2 <- fscores(mod2, full.scores.SE = TRUE)
# Combine factor scores and standard errors into data set
fs_dat <- as.data.frame(cbind(fs1, fs2))
names(fs_dat) <- c("fs1", "se_fs1", "fs2", "se_fs2")
# Compute reliability and error variances
fs_dat <- within(fs_dat, expr = {
  rel_fs1 <- 1 - se_fs1^2
  rel_fs2 <- 1 - se_fs2^2
  ev_fs1 <- se_fs1^2 * (1 - se_fs1^2)
  ev_fs2 <- se_fs2^2 * (1 - se_fs2^2)
})
# OpenMx model (from umx so that lavaan syntax can be used)
fsreg_umx <- umxLav2RAM(
  "
    fs2 ~ fs1
    fs2 + fs1 ~ 1
  ",
  printTab = FALSE)
# Prepare loading and error covariance matrices
cross_load <- matrix(c("rel_fs2", NA, NA, "rel_fs1"), nrow = 2) |>
  `dimnames<-`(rep(list(c("fs2", "fs1")), 2))
err_cov <- matrix(c("ev_fs2", NA, NA, "ev_fs1"), nrow = 2) |>
  `dimnames<-`(rep(list(c("fs2", "fs1")), 2))
# Create 2S-PA model (with definition variables)
tspa_mx <-
  tspa_mx_model(fsreg_umx,
    data = fs_dat,
    mat_ld = cross_load,
    mat_ev = err_cov
  )
# Run OpenMx
tspa_mx_fit <- mxRun(tspa_mx)
# Summarize the results
summary(tspa_mx_fit)

Gengrui-Zhang/R2spa documentation built on Sept. 6, 2024, 5:01 p.m.