Dataset data.hr (Ravand et al., 2013)
Dataset data.hr used for illustrating some functionalities
of the CDM package (Ravand, Barati, & Widhiarso, 2013).
data(data.hr)
The format of the dataset is:
List of 2 $ data : num [1:1550, 1:35] 1 0 1 1 1 0 1 1 1 0 ... $ q.matrix:'data.frame': ..$ Skill1: int [1:35] 0 0 0 0 0 0 1 0 0 0 ... ..$ Skill2: int [1:35] 0 0 0 0 1 0 0 0 0 0 ... ..$ Skill3: int [1:35] 0 1 1 1 1 0 0 1 0 0 ... ..$ Skill4: int [1:35] 1 0 0 0 0 0 0 0 1 1 ... ..$ Skill5: int [1:35] 0 0 0 0 0 1 0 0 1 1 ...
Simulated data according to Ravand et al. (2013).
Ravand, H., Barati, H., & Widhiarso, W. (2013). Exploring diagnostic capacity of a high stakes reading comprehension test: A pedagogical demonstration. Iranian Journal of Language Testing, 3(1), 1-27.
## Not run: data(data.hr, package="CDM") dat <- data.hr$data Q <- data.hr$q.matrix #************* # Model 1: DINA model mod1 <- CDM::din( dat, q.matrix=Q ) summary(mod1) # summary # plot results plot(mod1) # inspect coefficients coef(mod1) # posterior distribution posterior <- mod1$posterior round( posterior[ 1:5, ], 4 ) # first 5 entries # estimate class probabilities mod1$attribute.patt # individual classifications mod1$pattern[1:5,] # first 5 entries #************* # Model 2: GDINA model mod2 <- CDM::gdina( dat, q.matrix=Q) summary(mod2) #************* # Model 3: Reduced RUM model mod3 <- CDM::gdina( dat, q.matrix=Q, rule="RRUM" ) summary(mod3) #-------- # model comparisons # DINA vs GDINA anova( mod1, mod2 ) ## Model loglike Deviance Npars AIC BIC Chisq df p ## 1 Model 1 -31391.27 62782.54 101 62984.54 63524.49 195.9099 20 0 ## 2 Model 2 -31293.32 62586.63 121 62828.63 63475.50 NA NA NA # RRUM vs. GDINA anova( mod2, mod3 ) ## Model loglike Deviance Npars AIC BIC Chisq df p ## 2 Model 2 -31356.22 62712.43 105 62922.43 63483.76 125.7924 16 0 ## 1 Model 1 -31293.32 62586.64 121 62828.64 63475.50 NA NA NA # DINA vs. RRUM anova(mod1,mod3) ## Model loglike Deviance Npars AIC BIC Chisq df p ## 1 Model 1 -31391.27 62782.54 101 62984.54 63524.49 70.11246 4 0 ## 2 Model 2 -31356.22 62712.43 105 62922.43 63483.76 NA NA NA #------- # model fit # DINA fmod1 <- CDM::modelfit.cor.din( mod1, jkunits=0) summary(fmod1) ## Test of Global Model Fit ## type value p ## 1 max(X2) 16.35495 0.03125 ## 2 abs(fcor) 0.10341 0.01416 ## ## Fit Statistics ## est ## MADcor 0.01911 ## SRMSR 0.02445 ## MX2 0.93157 ## 100*MADRESIDCOV 0.39100 ## MADQ3 0.02373 # GDINA fmod2 <- CDM::modelfit.cor.din( mod2, jkunits=0) summary(fmod2) ## Test of Global Model Fit ## type value p ## 1 max(X2) 7.73670 1 ## 2 abs(fcor) 0.07215 1 ## ## Fit Statistics ## est ## MADcor 0.01830 ## SRMSR 0.02300 ## MX2 0.82584 ## 100*MADRESIDCOV 0.37390 ## MADQ3 0.02383 # RRUM fmod3 <- CDM::modelfit.cor.din( mod3, jkunits=0) summary(fmod3) ## Test of Global Model Fit ## type value p ## 1 max(X2) 15.49369 0.04925 ## 2 abs(fcor) 0.10076 0.02201 ## ## Fit Statistics ## est ## MADcor 0.01868 ## SRMSR 0.02374 ## MX2 0.87999 ## 100*MADRESIDCOV 0.38409 ## MADQ3 0.02416 ## End(Not run)
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