RaschPLE {plRasch} | R Documentation |
This function fits Rasch family models using pseudolikelihood esitmation. It is capable of dealing with polytomous items, and multidimensional latent variables.
RaschPLE(data, item.mtx, trait.mtx)
data |
is a data frame or matrix with rows indicating individuals, columns indicating items, and the entry values indicating the choices. |
item.mtx |
is the adjacency matrix between items and latent traits |
trait.mtx |
is the adjacency matrix for latent traits |
The model is
exp( w[i,h]' theta[v] + beta[i,h] )
P(X[v,i] = h) = ---------------------------------------------
sum_l exp( w[i,l]' theta[v] + beta[i,l] )
where
X[v,i] is the response of vth individual to ith item; w[i,h] is a vector of known category weights or scores for response h of ith item; theta[v] is a vector of latent traits for vth individual; beta[i,h] is the item difficulty parameter for ith item; associated with response h.
The function only returns the item parameter beta.
Essentially, it is a wrapper function: the equvialent llla model is fitted.
coefficients |
estimated item parameter beta |
se |
standard error of beta |
covb |
covariance matrix of the estimated parameter beta |
Zhushan "Mandy" Li & Feng Hong
Anderson, C.J., Li, Z., & Vermunt, J.K. (2007). Estimation of models in the Rasch family for polytomous items and multiple latent variables. Journal of Statistical Software, 20.
NCAT <- 2; NITEM <- 4; NEXAMINEE <- 50; BETA <- c(-1, 0, 0.5, 1) set.seed(1); rasch.sim <- simRasch(ncat=NCAT, nitem=NITEM, nexaminee=NEXAMINEE, beta=BETA) sim.data <- rasch.sim$data colnames(sim.data) <- paste("I", 1:NITEM, sep='') ## The model item adjacency matrix and the latent trait adjacency matrix item.mtx <- rep(1, NITEM); trait.mtx <- 1; plfit.rasch <- RaschPLE(sim.data, item.mtx, trait.mtx) plfit.rasch