Simulate from Generalized Logistic Item Response Model
This function simulates dichotomous item responses from a
generalized logistic item response model (Stukel, 1988).
The four-parameter logistic item response model
(Loken & Rulison, 2010) is a special case. See rasch.mml2
for more details.
sim.raschtype(theta, b, alpha1=0, alpha2=0, fixed.a=NULL, fixed.c=NULL, fixed.d=NULL)
theta |
Unidimensional ability vector θ |
b |
Vector of item difficulties b |
alpha1 |
Parameter α_1 in generalized logistic link function |
alpha2 |
Parameter α_2 in generalized logistic link function |
fixed.a |
Vector of item slopes a |
fixed.c |
Vector of lower item asymptotes c |
fixed.d |
Vector of lower item asymptotes d |
The class of generalized logistic link functions contain the most important link functions using the specifications (Stukel, 1988):
logistic link function: α_1=0 and α_2=0
probit link function: α_1=0.165 and α_2=0.165
loglog link function: α_1=-0.037 and α_2=0.62
cloglog link function: α_1=0.62 and α_2=-0.037
See pgenlogis
for exact transformation formulas of
the mentioned link functions.
Data frame with simulated item responses
Loken, E., & Rulison, K. L. (2010). Estimation of a four-parameter item response theory model. British Journal of Mathematical and Statistical Psychology, 63, 509-525.
Stukel, T. A. (1988). Generalized logistic models. Journal of the American Statistical Association, 83, 426-431.
############################################################################# ## EXAMPLE 1: Simulation of data from a Rasch model (alpha_1=alpha_2=0) ############################################################################# set.seed(9765) N <- 500 # number of persons I <- 11 # number of items b <- seq( -2, 2, length=I ) dat <- sirt::sim.raschtype( stats::rnorm( N ), b ) colnames(dat) <- paste0( "I", 1:I )
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