llla: Fit Log Linear by Linear Association Models

In plRasch: Log Linear by Linear Association models and Rasch family models by pseudolikelihood estimation

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

plRasch documentation built on May 2, 2019, 10:58 a.m.