gen.data: Generating Dichotomous Data Sets based on Logistic IRT Models...

In ConvertPar: Estimating IRT Parameters via Machine Learning Algorithms

Generating Dichotomous Data Sets based on Logistic IRT Models (Rasch, 2PL, 3PL).

Description

This function can be used for generating dichotomous response matrices based on Logistic IRT Models. Sample size, item number, parameter distributions can be specified.

Usage

gen.data(model="2PL",samplesize=1000,itemsize=100,
theta.mean=0,theta.sd=1, a.mean=0, a.sd=0.2,b.mean=0,
b.sd=1, c.min=0, c.max=0.25)

Arguments

model	string: option for desired IRT model. 'Rasch', '2PL' or '3PL' ('2PL' is default)
samplesize	numeric: Desired Sample size (Default 1000).
itemsize	numeric: Desired item number (Default 100).
theta.mean	numeric: mean value of theta normal distribution (Default 0).
theta.sd	numeric: standart deviation of theta normal distribution (Default 1).
a.mean	numeric: mean value of a parameters log normal distribution (Default 0).
a.sd	numeric: standart deviation of a parameters log normal distribution (Default 0.2).
b.mean	numeric: mean value of b parameters normal distribution (Default 0).
b.sd	numeric: standart deviation of b parameters normal distribution (Default 1).
c.min	numeric: minimum value of c parameters uniform distribution (Default 0).
c.max	numeric: maximum value of c parameters uniform distribution (Default 0.25).

Value

This function returns a a data frame containing simulated dichotomous response matrix.

Examples

 gen.data(model="2PL",
 samplesize=1000,
 itemsize=100,
 theta.mean=0,
 theta.sd=1,
 a.mean=0,
 a.sd=0.2,
 b.mean=0,
 b.sd=1,
 c.min=0,
 c.max=0.25)

ConvertPar documentation built on March 18, 2022, 7:07 p.m.