din.deterministic {CDM} R Documentation

## Deterministic Classification and Joint Maximum Likelihood Estimation of the Mixed DINA/DINO Model

### Description

This function allows the estimation of the mixed DINA/DINO model by joint maximum likelihood and a deterministic classification based on ideal latent responses.

### Usage

din.deterministic(dat, q.matrix, rule="DINA", method="JML", conv=0.001,
maxiter=300, increment.factor=1.05, progress=TRUE)


### Arguments

 dat Data frame of dichotomous item responses q.matrix Q-matrix with binary entries (see din). rule The condensation rule (see din). method Estimation method. The default is joint maximum likelihood estimation (JML). Other options include an adaptive estimation of guessing and slipping parameters (adaptive) while using these estimated parameters as weights in the individual deviation function and classification based on the Hamming distance (hamming) and the weighted Hamming distance (weighted.hamming) (see Chiu & Douglas, 2013). conv Convergence criterion for guessing and slipping parameters maxiter Maximum number of iterations increment.factor A numeric value of at least one which could help to improve convergence behavior and decreases parameter increments in every iteration. This option is disabled by setting this argument to 1. progress An optional logical indicating whether the function should print the progress of iteration in the estimation process.

### Value

A list with following entries

 attr.est Estimated attribute patterns criterion Criterion of the classification function. For joint maximum likelihood it is the deviance. guess Estimated guessing parameters slip Estimated slipping parameters prederror Average individual prediction error q.matrix Used Q-matrix dat Used data frame

### References

Chiu, C. Y., & Douglas, J. (2013). A nonparametric approach to cognitive diagnosis by proximity to ideal response patterns. Journal of Classification, 30, 225-250.

### See Also

For estimating the mixed DINA/DINO model using marginal maximum likelihood estimation see din.

See also the NPCD::JMLE function in the NPCD package for joint maximum likelihood estimation of the DINA or the DINO model.

### Examples

#############################################################################
# EXAMPLE 1: 13 items and 3 attributes
#############################################################################

set.seed(679)
N <- 3000
# specify true Q-matrix
q.matrix <- matrix( 0, 13, 3 )
q.matrix[1:3,1] <- 1
q.matrix[4:6,2] <- 1
q.matrix[7:9,3] <- 1
q.matrix[10,] <- c(1,1,0)
q.matrix[11,] <- c(1,0,1)
q.matrix[12,] <- c(0,1,1)
q.matrix[13,] <- c(1,1,1)
q.matrix <- rbind( q.matrix, q.matrix )
colnames(q.matrix) <- paste0("Attr",1:ncol(q.matrix))

# simulate data according to the DINA model
dat <- CDM::sim.din( N=N, q.matrix)$dat # Joint maximum likelihood estimation (the default: method="JML") res1 <- CDM::din.deterministic( dat, q.matrix ) # Adaptive estimation of guessing and slipping parameters res <- CDM::din.deterministic( dat, q.matrix, method="adaptive" ) # Classification using Hamming distance res <- CDM::din.deterministic( dat, q.matrix, method="hamming" ) # Classification using weighted Hamming distance res <- CDM::din.deterministic( dat, q.matrix, method="weighted.hamming" ) ## Not run: #********* load NPCD library for JML estimation library(NPCD) # DINA model res <- NPCD::JMLE( Y=dat[1:100,], Q=q.matrix, model="DINA" ) as.data.frame(res$par.est )   # item parameters
res$alpha.est # skill classifications # RRUM model res <- NPCD::JMLE( Y=dat[1:100,], Q=q.matrix, model="RRUM" ) as.data.frame(res$par.est )

## End(Not run)


[Package CDM version 8.2-6 Index]