GRShiny"

In GRShiny: Graded Response Model

knitr::opts_chunk$set(
  eval = T,
  collapse = TRUE,
  out.width = "50%",
  fig.width = 7,
  comment = "#>"
)

library(GRShiny)

GRM data simulation

Item parameters for graded response model

item_pars <- genIRTpar(nitem = 10, ncat = 3, nfac = 1)

Individual true latent traits

true_theta <- genTheta(nsample = 500, nfac = 1)

GRM data

grm_dt <- genData(eta = true_theta, ipar = item_pars)

GRM data simulation

Generate lavaan syntax

lav_syn <- genLavSyn(dat = grm_dt, nfac = 1)

Conduct GRM with two different estimators

grm.fit <- runGRM(dat = grm_dt, lav.syntax = lav_syn, estimator = "WL")

Results

parameter estimates

extract_est(grm.fit)

IRT plots

ICCplot(grm.fit, 1)
ESplot(grm.fit , 1)
infoPlot(grm.fit, 1)
FSplot(grm.fit)

Launch app

startGRshiny()

GRShiny documentation built on May 4, 2023, 1:07 a.m.