TAM (version 2.4-9)

data.sim.rasch: Simulated Rasch data

Description

Simulated Rasch data under unidimensional trait distribution

Usage

data(data.sim.rasch) 
 data(data.sim.rasch.pweights)
 data(data.sim.rasch.missing)

Format

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" ...

Details

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 ) )

Examples

# 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 )
  
# }