# Deterministic Classification and Joint Maximum Likelihood Estimation of the Mixed DINA/DINO Model This function allows the estimation of the mixed DINA/DINO model by joint maximum likelihood and a deterministic classification based on ideal latent responses. ```r 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. ## Returns 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 ```r ############################################################################# # 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) ```

din.deterministic function