sim_model | R Documentation |
Simulates an item response model given a fitted object or input of item response probabilities and skill class probabilities.
sim_model(object=NULL, irfprob=NULL, theta_index=NULL, prob.theta=NULL, data=NULL, N_sim=NULL )
object |
Fitted object for which the methods |
irfprob |
Array of item response function values (items \times categories \times skill classes) |
theta_index |
Skill class index for sampling |
prob.theta |
Skill class probabilities |
data |
Original dataset, only relevant for simulating item response pattern with missing values |
N_sim |
Number of subjects to be simulated |
List containing elements
dat |
Simulated item responses |
theta |
Simulated skill classes |
theta_index |
Corresponding indices to |
## Not run: ############################################################################# # EXAMPLE 1: GDINA model simulation ############################################################################# data(sim.dina, package="CDM") data(sim.qmatrix, package="CDM") dat <- sim.dina Q <- sim.qmatrix # fit DINA model mod <- CDM::gdina( dat, q.matrix=Q, rule="DINA") summary(mod) #** simulate new item responses (N equals observed sample size) dat1 <- CDM::sim_model(mod) #*** simulate item responses for N=2000 subjects dat2 <- CDM::sim_model(mod, N_sim=2000) str(dat2) #*** simulate item responses based on input item response probabilities #*** and theta_index irfprob <- CDM::IRT.irfprob(mod) prob.theta <- attr(irfprob, "prob.theta") TP <- length(prob.theta) theta_index <- sample(1:TP, size=1000, prob=prob.theta, replace=TRUE ) #-- simulate dat3 <- CDM::sim_model(irfprob=irfprob, theta_index=theta_index) str(dat3) ## End(Not run)
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