TAM (version 3.1-45)

anova-logLik: Likelihood Ratio Test for Model Comparisons and Log-Likelihood Value

Description

The anova function compares two models estimated of class tam, tam.mml or tam.mml.3pl using a likelihood ratio test. The logLik function extracts the value of the log-Likelihood.

The function can be applied for values of tam.mml, tam.mml.2pl, tam.mml.mfr, tam.fa, tam.mml.3pl, tam.latreg or tamaan.

Usage

# S3 method for tam
anova(object, …)
# S3 method for tam
logLik(object, …)
# S3 method for tam.mml
anova(object, …)
# S3 method for tam.mml
logLik(object, …)
# S3 method for tam.mml.3pl
anova(object, …)
# S3 method for tam.mml.3pl
logLik(object, …)
# S3 method for tamaan
anova(object, …)
# S3 method for tamaan
logLik(object, …)
# S3 method for tam.latreg
anova(object, …)
# S3 method for tam.latreg
logLik(object, …)
# S3 method for tam.np
anova(object, …)
# S3 method for tam.np
logLik(object, …)

Arguments

object

Object of class tam, tam.mml, tam.mml.3pl, tam.latreg, tam.np, or tamaan. Note that for anova two objects (fitted models) must be provided.

…

Further arguments to be passed

Value

A data frame containing the likelihood ratio test statistic and information criteria.

Examples

# NOT RUN {
#############################################################################
# EXAMPLE 1: Dichotomous data sim.rasch - 1PL vs. 2PL model
#############################################################################

data(data.sim.rasch)
# 1PL estimation
mod1 <- TAM::tam.mml(resp=data.sim.rasch)
logLik(mod1)
# 2PL estimation
mod2 <- TAM::tam.mml.2pl(resp=data.sim.rasch, irtmodel="2PL")
logLik(mod2)
# Model comparison
anova( mod1, mod2 )
  ##     Model   loglike Deviance Npars      AIC      BIC    Chisq df       p
  ##   1  mod1 -42077.88 84155.77    41 84278.77 84467.40 54.05078 39 0.05508
  ##   2  mod2 -42050.86 84101.72    80 84341.72 84709.79       NA NA      NA

# }
# NOT RUN {
#############################################################################
# EXAMPLE 2: Dataset reading (sirt package): 1- vs. 2-dimensional model
#############################################################################

data(data.read,package="sirt")

# 1-dimensional model
mod1 <- TAM::tam.mml.2pl(resp=data.read )
# 2-dimensional model
mod2 <- TAM::tam.fa(resp=data.read, irtmodel="efa", nfactors=2,
             control=list(maxiter=150) )
# Model comparison
anova( mod1, mod2 )
  ##       Model   loglike Deviance Npars      AIC      BIC    Chisq df  p
  ##   1    mod1 -1954.888 3909.777    24 3957.777 4048.809 76.66491 11  0
  ##   2    mod2 -1916.556 3833.112    35 3903.112 4035.867       NA NA NA
# }