CDM (version 4.991-1)

data.hr: Dataset data.hr (Ravand et al., 2013)

Description

Dataset data.hr used for illustrating some functionalities of the CDM package (Ravand, Barati, & Widhiarso, 2013).

Usage

data(data.hr)

Format

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 ...

Source

Simulated data according to Ravand et al. (2013).

References

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.

Examples

## Not run: 
# 
# data(data.hr)
# 
# #*************
# # Model 1: DINA model
# mod1 <- CDM::din( data.hr$data , q.matrix = data.hr$q.matrix )
# 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$pattern[1:5,]   # first 5 entries
# 
# #*************
# # Model 2: GDINA model
# mod2 <- CDM::gdina( data.hr$data , q.matrix = data.hr$q.matrix )
# summary(mod2)  
# 
# #*************
# # Model 3: Reduced RUM model
# mod3 <- CDM::gdina( data.hr$data , q.matrix = data.hr$q.matrix , 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)