R: Generation of Simulated Item Response Data

gendataIRT {lazy.irtx}

R Documentation

Generation of Simulated Item Response Data

Description

Generation of Simulated Item Response Data

Usage

gendataIRT(Ntot, param, DinP = 1, Nmat = NULL, theta = NULL,
  npoints = 31, thmin = -3, thmax = 3, thd = NULL, thdist = "NORMAL",
  thmean = 0, thstd = 1, nomiss = 0, compress = 0, sort = 1,
  thetaname = NULL)

Arguments

`Ntot`	# of total observations to be generated If Ntot == 1, one observation per each theta point will be generated, otherwize, the # of observations per each theta point is proportional to round(Ntot*thd) where thd is the npoint x 1 vector of theta distribution. Therefore, Ntot must be large enough.
`param`	Item Parameter Data Frame
`DinP`	= 1 to include D=1.7 in logistic function
`Nmat`	npoints x nitems matrix consisting of # of trials, or NULL. This will be used only when Ntot=1 and compress=0.
`theta`	Discrete theta values
`npoints`	# of discrete points for theta, or # of random variables
`thmin`	Minimum value of discrete thata value
`thmax`	Maximum value of discrete thata value
`thd`	theta distribution probability vector
`thdist`	= "NORMAL" or "UNIFORM" or "rnorm" or "runif" When "NORMAL" or "rnorm", thmean and thstd will be used to generate thd. When "UNIFORM" or "runif", thmin and thmax will be used to generate thd. When "rnorm" or "runif", theta will be generated using npoints random numbers and thd and Ntot is set equal to 1.
`thmean`	mean of theta distribution
`thstd`	std of theta distribution
`nomiss`	= 1 to exclude those theta points with N = 0.
`compress`	= 1 to compress the data when Ntot = 1.
`sort`	= 1 to sort the result according to theta.
`thetaname`	= Name of the variable which will contain theta value or NULL.

Value

A list of
U, N, npoints, theta, thd, Ntot, fromP, toP, type, ncat, thmean, thstd , compress, sort

where

If compress = 0 and Ntot is large,
U is npoints x sum of ncat[j]
U[,fromP[j]:toP[j]] is npoints x ncat[j]
U[,fromP[j]:toP[j]][i,k] contains # of responses to
the k-th category (k=0,1,...,ncat[j]) at theta[i]
to which N[i]=round(Ntot*thd[i]) observations belong.

If compress = 1 and Ntot = 1,
U is npoints x nitems
U[i,j] contains the response to the j-th item at theta[i]
where 0 <= U[i,j] <= ncat[j]-1.
In this case, fromP and toP are not compressed.

(theta, thd, N) are npoints x 1
N[i] is the # of observations at theta[i]

Examples

# 10 observations with theta from N( 0, 1 )
set.seed(1701)
res1 <- gendataIRT( 1, paramS1, npoints=10, thdist="rnorm", compress=1 )
Print(res1$N,res1$U,sum(res1$N))

set.seed(1701)
# 100 observations tabulated at seven theta points in [-3,3]
res <- gendataIRT( 1000, paramS1, npoints=7, thmin=-3, thmax=3 )
Print(res$N,res$U,sum(res$N))

# seven theta points in [-3,3]
set.seed(1701)
res1 <- gendataIRT( 1, paramS1, npoints=7, thmin=-3, thmax=3 )
Print(res1$N,res1$U,sum(res1$N))

# seven theta points in [-3,3] and compressed
set.seed(1701)
res1 <- gendataIRT( 1, paramS1, npoints=7, thmin=-3, thmax=3, compress=1 )
Print(res1$N,res1$U,sum(res1$N))

# 10 observations with theta from N( 0, 1 )
set.seed(1701)
res1 <- gendataIRT( 1, paramS1, npoints=10, thdist="rnorm", compress=1
                  , thetaname="theta" )
Print(res1$N,res1$U,sum(res1$N))

# Using Nmat argument
set.seed(1701)
npoints=20
param=paramS1
nitems=nrow(param)
Nmat=matrix(100,npoints,nitems)
res1 <- gendataIRT( 1, param, Nmat=Nmat, npoints=npoints
                  , thdist="rnorm", compress=1 )
Print(res1$N,res1$U,sum(res1$N))

[Package lazy.irtx version 1.0.1 Index]