Last chance! 50% off unlimited learning
Sale ends in
din
, gdina
or gdm
using a likelihood ratio test.## S3 method for class 'din':
anova(object,\dots)
## S3 method for class 'gdina':
anova(object,\dots)
## S3 method for class 'mcdina':
anova(object,\dots)
## S3 method for class 'gdm':
anova(object,\dots)
## S3 method for class 'slca':
anova(object,\dots)
din
, gdina
, mcdina
,
slca
or gdm
din
, gdina
, gdm
,
mcdina
, slca
#############################################################################
# EXAMPLE 1: anova with din objects
#############################################################################
# Model 1
d1 <- din(sim.dina, q.matr = sim.qmatrix )
# Model 2 with equal guessing and slipping parameters
d2 <- din(sim.dina, q.matr = sim.qmatrix , guess.equal=TRUE , slip.equal =TRUE)
# model comparison
anova(d1,d2)
## Model loglike Deviance Npars AIC BIC Chisq df p
## 2 d2 -2176.482 4352.963 9 4370.963 4406.886 268.2071 16 0
## 1 d1 -2042.378 4084.756 25 4134.756 4234.543 NA NA NA
#############################################################################
# EXAMPLE 2: anova with gdina objects
#############################################################################
# Model 3: GDINA model
d3 <- gdina( sim.dina, q.matr = sim.qmatrix )
# Model 4: DINA model
d4 <- gdina( sim.dina, q.matr = sim.qmatrix , rule="DINA")
# model comparison
anova(d3,d4)
## Model loglike Deviance Npars AIC BIC Chisq df p
## 2 d4 -2042.293 4084.586 25 4134.586 4234.373 31.31995 16 0.01224
## 1 d3 -2026.633 4053.267 41 4135.266 4298.917 NA NA NA
Run the code above in your browser using DataLab