Simulated Rasch data
Simulated Rasch data under unidimensional trait distribution
data(data.sim.rasch) data(data.sim.rasch.pweights) data(data.sim.rasch.missing)
The format is: num [1:2000, 1:40] 1 0 1 1 1 1 1 1 1 1 ...
- attr(*, "dimnames")=List of 2
..$ : NULL
..$ : chr [1:40] "I1" "I2" "I3" "I4" ...
N <- 2000
# simulate predictors
Y <- cbind( stats::rnorm( N, sd=1.5), stats::rnorm(N, sd=.3 ) )
theta <- stats::rnorm( N ) + .4 * Y[,1] + .2 * Y[,2] # latent regression model
# simulate item responses with missing data
I <- 40
resp[ theta < 0, c(1,seq(I/2+1, I)) ] <- NA
# define person weights
pweights <- c( rep(3,N/2), rep( 1, N/2 ) )
Simulated data (see Details)
## Not run: data(data.sim.rasch) N <- 2000 Y <- cbind( stats::rnorm( N, sd=1.5), stats::rnorm(N, sd=.3 ) ) # Loading Matrix # B <- array( 0, dim=c( I, 2, 1 ) ) # B[1:(nrow(B)), 2, 1] <- 1 B <- TAM::designMatrices(resp=data.sim.rasch)[["B"]] # estimate Rasch model mod1_1 <- TAM::tam.mml(resp=data.sim.rasch, Y=Y) # standard errors res1 <- TAM::tam.se(mod1_1) # Compute fit statistics tam.fit(mod1_1) # plausible value imputation # PV imputation has to be adpated for multidimensional case! pv1 <- TAM::tam.pv( mod1_1, nplausible=7, # 7 plausible values samp.regr=TRUE # sampling of regression coefficients ) # item parameter constraints xsi.fixed <- matrix( c( 1, -2,5, -.22,10, 2 ), nrow=3, ncol=2, byrow=TRUE) xsi.fixed mod1_4 <- TAM::tam.mml( resp=data.sim.rasch, xsi.fixed=xsi.fixed ) # missing value handling data(data.sim.rasch.missing) mod1_2 <- TAM::tam.mml(data.sim.rasch.missing, Y=Y) # handling of sample (person) weights data(data.sim.rasch.pweights) N <- 1000 pweights <- c( rep(3,N/2), rep( 1, N/2 ) ) mod1_3 <- TAM::tam.mml( data.sim.rasch.pweights, control=list(conv=.001), pweights=pweights ) ## End(Not run)
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