# NOT RUN {
#############################################################################
# EXAMPLE 1: Model comparison sim.dina dataset
#############################################################################
data(sim.dina, package="CDM")
data(sim.qmatrix, package="CDM")
dat <- sim.dina
q.matrix <- sim.qmatrix
#*** Model 0: DINA model with equal guessing and slipping parameters
mod0 <- CDM::din( dat, q.matrix, guess.equal=TRUE, slip.equal=TRUE )
summary(mod0)
#*** Model 1: DINA model
mod1 <- CDM::din( dat, q.matrix )
summary(mod1)
#*** Model 2: DINO model
mod2 <- CDM::din( dat, q.matrix, rule="DINO")
summary(mod2)
#*** Model 3: Additive GDINA model
mod3 <- CDM::gdina( dat, q.matrix, rule="ACDM")
summary(mod3)
#*** Model 4: GDINA model
mod4 <- CDM::gdina( dat, q.matrix, rule="GDINA")
summary(mod4)
# model comparisons
res <- CDM::IRT.compareModels( mod0, mod1, mod2, mod3, mod4 )
res
## > res
## $IC
## Model loglike Deviance Npars Nobs AIC BIC AIC3 AICc CAIC
## 1 mod0 -2176.482 4352.963 9 400 4370.963 4406.886 4379.963 4371.425 4415.886
## 2 mod1 -2042.378 4084.756 25 400 4134.756 4234.543 4159.756 4138.232 4259.543
## 3 mod2 -2086.805 4173.610 25 400 4223.610 4323.396 4248.610 4227.086 4348.396
## 4 mod3 -2048.233 4096.466 32 400 4160.466 4288.193 4192.466 4166.221 4320.193
## 5 mod4 -2026.633 4053.266 41 400 4135.266 4298.917 4176.266 4144.887 4339.917
##
# -> The DINA model (mod1) performed best in terms of AIC.
## $LRtest
## Model1 Model2 Chi2 df p
## 1 mod0 mod1 268.20713 16 0.000000e+00
## 2 mod0 mod2 179.35362 16 0.000000e+00
## 3 mod0 mod3 256.49745 23 0.000000e+00
## 4 mod0 mod4 299.69671 32 0.000000e+00
## 5 mod1 mod3 -11.70967 7 1.000000e+00
## 6 mod1 mod4 31.48959 16 1.164415e-02
## 7 mod2 mod3 77.14383 7 5.262457e-14
## 8 mod2 mod4 120.34309 16 0.000000e+00
## 9 mod3 mod4 43.19926 9 1.981445e-06
##
# -> The GDINA model (mod4) was superior to the other models in terms
# of the likelihood ratio test.
# get an overview with summary
summary(res)
summary(res,extended=FALSE)
#*******************
# applying model comparison for objects of class IRT.modelfit
# compute model fit statistics
fmod0 <- CDM::IRT.modelfit(mod0)
fmod1 <- CDM::IRT.modelfit(mod1)
fmod4 <- CDM::IRT.modelfit(mod4)
# model comparison
res <- CDM::IRT.compareModels( fmod0, fmod1, fmod4 )
res
## $IC
## Model loglike Deviance Npars Nobs AIC BIC AIC3
## mod0 mod0 -2176.482 4352.963 9 400 4370.963 4406.886 4379.963
## mod1 mod1 -2042.378 4084.756 25 400 4134.756 4234.543 4159.756
## mod4 mod4 -2026.633 4053.266 41 400 4135.266 4298.917 4176.266
## AICc CAIC maxX2 p_maxX2 MADcor SRMSR
## mod0 4371.425 4415.886 118.172707 0.0000000 0.09172287 0.10941300
## mod1 4138.232 4259.543 8.728248 0.1127943 0.03025354 0.03979948
## mod4 4144.887 4339.917 2.397241 1.0000000 0.02284029 0.02989669
## X100.MADRESIDCOV MADQ3 MADaQ3
## mod0 1.9749936 0.08840892 0.08353917
## mod1 0.6713952 0.06184332 0.05923058
## mod4 0.5148707 0.07477337 0.07145600
##
## $LRtest
## Model1 Model2 Chi2 df p
## 1 mod0 mod1 268.20713 16 0.00000000
## 2 mod0 mod4 299.69671 32 0.00000000
## 3 mod1 mod4 31.48959 16 0.01164415
# }
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