CDM: din.deterministic – R documentation

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din.deterministic

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.

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)

CDM

Cognitive Diagnosis Modeling

v7.5-15

GPL (>= 2)

Authors

Alexander Robitzsch [aut, cre], Thomas Kiefer [aut], Ann Cathrice George [aut], Ali Uenlue [aut]

Initial release

2020-03-10 14:19:21