cdmTools (version 1.0.5)

GNPC: General nonparametric classification method

Description

Attribute profile estimation using the general nonparametric classification method (GNPC; Chiu, Sun, & Bian, 2018). The GNPC can be considered as a robust alternative to the parametric G-DINA model with low sample sizes. The AlphaNP function from the NPCD package (Zheng & Chiu, 2019; Chiu, Sun, & Bian, 2018) using weighted Hamming distances is used to initiate the procedure.

Usage

GNPC(
  dat,
  Q,
  initiate = "AND",
  min.change = 0.001,
  maxitr = 1000,
  verbose = TRUE
)

Value

GNPC returns an object of class GNPC.

alpha.est

Estimated attribute profiles (matrix).

loss.matrix

The distances between the weighted ideal responses from each latent class (rows) and examinees' observed responses (columns) (matrix).

eta.w

The weighted ideal responses for each latent class (rows) on each item (columns) (matrix).

w

The estimated weights, used to compute the weighted ideal responses (matrix).

n.ite

Number of iterations required to achieve convergence (double).

hist.change

Proportion of modified attribute profiles in each iteration (vector).

specifications

Function call specifications (list).

Arguments

dat

A N individuals x J items (matrix or data.frame). Missing values need to be coded as NA. Caution is advised if missing data are present.

Q

A J items x K attributes Q-matrix (matrix or data.frame).

initiate

Should the conjunctive ("AND") or disjunctive ("OR") NPC be used to initiate the procedure? Default is "AND".

min.change

Minimum proportion of modified attribute profiles to use as a stopping criterion. Default is .001.

maxitr

Maximum number of iterations. Default is 1000.

verbose

Print information after each iteration. Default is TRUE.

Author

Pablo Nájera, Universidad Pontificia Comillas

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. DOI: 10.1007/s00357-013-9132-9

Chiu, C.-Y., Sun, Y., & Bian, Y. (2018). Cognitive diagnosis for small education programs: The general nonparametric classification method. Psychometrika, 83, 355-375. DOI: 10.1007/s11336-017-9595-4

Zheng, Y., & Chiu, C.-Y. (2019). NPCD: Nonparametric methods for cognitive diagnosis. R package version 1.0-11. https://cran.r-project.org/web/packages/NPCD/.

Examples

# \donttest{
library(GDINA)
Q <- sim30GDINA$simQ # Q-matrix
K <- ncol(Q)
J <- nrow(Q)
set.seed(123)
GS <- data.frame(guessing = rep(0.1, J), slip = rep(0.1, J))
sim <- simGDINA(200, Q, GS)
simdat <- sim$dat # Simulated data
simatt <- sim$attribute # Generating attributes
fit.GNPC <- GNPC(simdat, Q) # Apply the GNPC method
ClassRate(fit.GNPC$alpha.est, simatt) # Check classification accuracy
# }