TAM (version 1.6-0)

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 class 'tam':
anova(object, \dots)
## S3 method for class 'tam':
logLik(object, \dots)

## S3 method for class 'tam.mml':
anova(object, \dots)
## S3 method for class 'tam.mml':
logLik(object, \dots)

## S3 method for class 'tam.mml.3pl':
anova(object, \dots)
## S3 method for class 'tam.mml.3pl':
logLik(object, \dots)

## S3 method for class 'tamaan':
anova(object, \dots)
## S3 method for class 'tamaan':
logLik(object, \dots)

## S3 method for class 'tam.latreg':
anova(object, \dots)
## S3 method for class 'tam.latreg':
logLik(object, \dots)

Arguments

object

Object of class tam, tam.mml, tam.mml.3pl, tam.latreg

...

Further arguments to be passed

Value

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

Examples

#############################################################################
# EXAMPLE 1: Dichotomous data sim.rasch - 1PL vs. 2PL model
#############################################################################
	
data(sim.rasch)
# 1PL estimation
mod1 <- tam.mml(resp=sim.rasch)
logLik(mod1)
# 2PL estimation
mod2 <- tam.mml.2pl(resp=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
  
#############################################################################
# EXAMPLE 2: Dataset reading (sirt package): 1- vs. 2-dimensional model
#############################################################################

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

# 1-dimensional model
mod1 <- tam.mml.2pl(resp= data.read ) 
# 2-dimensional model
mod2 <- 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