IRT.simulate: Simulating Item Response Models

IRT.simulateR Documentation

Simulating Item Response Models

Description

Defines an S3 method for simulation of item response models.

Usage

IRT.simulate(object, ...)

## S3 method for class 'tam.mml'
IRT.simulate(object, iIndex=NULL, theta=NULL, nobs=NULL, ...)

## S3 method for class 'tam.mml.2pl'
IRT.simulate(object, iIndex=NULL, theta=NULL, nobs=NULL, ...)

## S3 method for class 'tam.mml.mfr'
IRT.simulate(object, iIndex=NULL, theta=NULL, nobs=NULL, ...)

## S3 method for class 'tam.mml.3pl'
IRT.simulate(object, iIndex=NULL, theta=NULL, nobs=NULL, ...)

Arguments

object

An object of class tam.mml, tam.mml.2pl, tam.mml.mfr or tam.mml.3pl.

iIndex

Optional vector of item indices

theta

Optional matrix of theta values

nobs

Optional numeric containing the number of observations to be simulated.

...

Further objects to be passed

Value

Data frame with simulated item responses

Examples

#############################################################################
# EXAMPLE 1: Simulating Rasch model
#############################################################################

data(data.sim.rasch)

#** (1) estimate model
mod1 <- TAM::tam.mml(resp=data.sim.rasch )

#** (2) simulate data
sim.dat <- TAM::IRT.simulate(mod1)

## Not run: 
#** (3) use a subset of items with the argument iIndex
set.seed(976)
iIndex <- sort(sample(ncol(data.sim.rasch), 15))  # randomly select 15 items
sim.dat <- TAM::IRT.simulate(mod1, iIndex=iIndex)
mod.sim.dat <- TAM::tam.mml(sim.dat)

#** (4) specify number of persons in addition
sim.dat <- TAM::IRT.simulate(mod1, nobs=1500, iIndex=iIndex)

# Rasch - constraint="items" ----
mod1 <- TAM::tam.mml(resp=data.sim.rasch,  constraint="items",
                control=list( xsi.start0=1, fac.oldxsi=.5) )

# provide abilities
theta0 <- matrix( rnorm(1500, mean=0.5, sd=sqrt(mod1$variance)), ncol=1 )
# simulate data
data <- TAM::IRT.simulate(mod1, theta=theta0 )
# estimate model based on simulated data
xsi.fixed <- cbind(1:nrow(mod1$item), mod1$item$xsi.item)
mod2 <- TAM::tam.mml(data, xsi.fixed=xsi.fixed )
summary(mod2)

#############################################################################
# EXAMPLE 2: Simulating 2PL model
#############################################################################

data(data.sim.rasch)
# estimate 2PL
mod2 <- TAM::tam.mml.2pl(resp=data.sim.rasch, irtmodel="2PL")
# simulate 2PL
sim.dat <- TAM::IRT.simulate(mod2)
mod.sim.dat <- TAM::tam.mml.2pl(resp=sim.dat, irtmodel="2PL")

#############################################################################
# EXAMPLE 3: Simulate multiple group model
#############################################################################

# Multi-Group ----
set.seed(6778)
N <- 3000
theta <- c( stats::rnorm(N/2,mean=0,sd=1.5), stats::rnorm(N/2,mean=.5,sd=1)  )
I <- 20
p1 <- stats::plogis( outer( theta, seq( -2, 2, len=I ), "-" ) )
resp <- 1 * ( p1 > matrix( stats::runif( N*I ), nrow=N, ncol=I ) )
colnames(resp) <- paste("I", 1:I, sep="")
group <- rep(1:2, each=N/2 )
mod3 <- TAM::tam.mml(resp, group=group)

# simulate data
sim.dat.g1 <- TAM::IRT.simulate(mod3,
                   theta=matrix( stats::rnorm(N/2, mean=0, sd=1.5), ncol=1) )
sim.dat.g2 <- TAM::IRT.simulate(mod3,
                   theta=matrix( stats::rnorm(N/2, mean=.5, sd=1), ncol=1) )
sim.dat <- rbind( sim.dat.g1, sim.dat.g2)
# estimate model
mod3s <- TAM::tam.mml( sim.dat, group=group)

#############################################################################
# EXAMPLE 4: Multidimensional model and latent regression
#############################################################################

set.seed(6778)
N <- 2000
Y <- cbind( stats::rnorm(N), stats::rnorm(N))
theta <- mvtnorm::rmvnorm(N, mean=c(0,0), sigma=matrix(c(1,.5,.5,1), 2, 2))
theta[,1] <- theta[,1] + .4 * Y[,1] + .2 * Y[,2]  # latent regression model
theta[,2] <- theta[,2] + .8 * Y[,1] + .5 * Y[,2]  # latent regression model
I <- 20
p1 <- stats::plogis(outer(theta[, 1], seq(-2, 2, len=I), "-"))
resp1 <- 1 * (p1 > matrix(stats::runif(N * I), nrow=N, ncol=I))
p1 <- stats::plogis(outer(theta[, 2], seq(-2, 2, len=I ), "-" ))
resp2 <- 1 * (p1 > matrix(stats::runif(N * I), nrow=N, ncol=I))
resp <- cbind(resp1, resp2)
colnames(resp) <- paste("I", 1 : (2 * I), sep="")

# (2) define loading Matrix
Q <- array(0, dim=c(2 * I, 2))
Q[cbind(1:(2*I), c(rep(1, I), rep(2, I)))] <- 1
Q

# (3) estimate models
mod4 <- TAM::tam.mml(resp=resp, Q=Q, Y=Y, control=list(  maxiter=15))

# simulate new item responses
theta <- mvtnorm::rmvnorm(N, mean=c(0,0), sigma=matrix(c(1,.5,.5,1), 2, 2))
theta[,1] <- theta[,1] + .4 * Y[,1] + .2 * Y[,2]  # latent regression model
theta[,2] <- theta[,2] + .8 * Y[,1] + .5 * Y[,2]  # latent regression model

sim.dat <- TAM::IRT.simulate(mod4, theta=theta)
mod.sim.dat <- TAM::tam.mml(resp=sim.dat, Q=Q, Y=Y, control=list( maxiter=15))

## End(Not run)

TAM documentation built on May 15, 2022, 1:05 a.m.