llla {plRasch}R Documentation

Fitting Log Linear by Linear Association models

Description

This function fits log linear by linear association models using pseudolikelihood method.

Usage

llla(data, item.mtx = rep(1, ncol(data)), trait.mtx = 1, useMLE = FALSE, uncorrected = FALSE)

Arguments

data is a data frame or matrix with rows indicating individuals and columns indicating items and the values indicating the choices.
item.mtx is the adjacency matrix between items and the latent traits
trait.mtx is the adjacency matrix for latent traits
useMLE inidicates whether maximum likelihood estimation is used
uncorrected if the value is TRUE, calculate the uncorrected standard errors

Value

coefficients the parameter estimates in the LLLA model
se the standard error of coefficient esimates(sandwich estimator)
covb the covariance matrix of the coefficient esimates
se.uncorrected the standard error not corrected
ncat number of categories
nexaminee number of examinees
nitem number of items

Author(s)

Zhushan "Mandy" Li & Feng Hong

References

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.

See Also

simRasch

Examples

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;

### MLE of log-multiplicative Assoc. Model
mlfit <- llla(sim.data, item.mtx, trait.mtx, useMLE=TRUE)
mlfit

#### PLE of log-multiplicative Assoc. Model
plfit <- llla(sim.data, item.mtx, trait.mtx)
plfit


[Package plRasch version 0.1 Index]