An (experimental) function for calibrating the multiple-strategy CDMs for dichotomous response data (Ma & Guo, 2019)

GMSCDM(
  dat,
  msQ,
  model = "ACDM",
  s = 1,
  att.prior = NULL,
  delta = NULL,
  control = list()
)

Arguments

dat

A required binary item response matrix

msQ

A multiple-strategy Q-matrix; the first column gives item numbers and the second column gives the strategy number. See examples.

model

CDM used; can be "DINA","DINO","ACDM","LLM", and "RRUM", representing the GMS-DINA, GMS-DINO, GMS-ACDM, GMS-LLM and GMS-RRUM in Ma & Guo (2019), respectively. It can also be "rDINA" and "rDINO", representing restricted GMS-DINA and GMS-DINO models where delta_jm1 are equal for all strategies. Note that only a single model can be used for the whole test.

s

strategy selection parameter. It is equal to 1 by default.

att.prior

mixing proportion parameters.

delta

delta parameters in list format.

control

a list of control arguments

Value

an object of class GMSCDM with the following components:

IRF

A matrix of success probabilities for each latent class on each item (IRF)

delta

A list of delta parameters

attribute

A list of estimated attribute profiles including EAP, MLE and MAP estimates.

testfit

A list of test fit statistics including deviance, number of parameters, AIC and BIC

sIRF

strategy-specific item response function

pjmc

Probability of adopting each strategy on each item for each latent class

sprv

Strategy pravelence

References

Ma, W., & de la Torre, J. (2020). GDINA: An R Package for Cognitive Diagnosis Modeling. Journal of Statistical Software, 93(14), 1-26.

Ma, W., & Guo, W. (2019). Cognitive Diagnosis Models for Multiple Strategies. British Journal of Mathematical and Statistical Psychology.

See also

GDINA for MS-DINA model and single strategy CDMs, and DTM for diagnostic tree model for multiple strategies in polytomous response data

Examples

if (FALSE) { ################## # # data simulation # ################## set.seed(123) msQ <- matrix( c(1,1,0,1, 1,2,1,0, 2,1,1,0, 3,1,0,1, 4,1,1,1, 5,1,1,1),6,4,byrow = T) # J x L - 00,10,01,11 LC.prob <- matrix(c( 0.2,0.7727,0.5889,0.8125, 0.1,0.9,0.1,0.9, 0.1,0.1,0.8,0.8, 0.2,0.5,0.4,0.7, 0.2,0.4,0.7,0.9),5,4,byrow=TRUE) N <- 10000 att <- sample(1:4,N,replace=TRUE) dat <- 1*(t(LC.prob[,att])>matrix(runif(N*5),N,5)) est <- GMSCDM(dat,msQ) # item response function est$IRF # strategy specific IRF est$sIRF ################################ # # Example 14 from GDINA function # ################################ Q <- matrix(c(1,1,1,1,0, 1,2,0,1,1, 2,1,1,0,0, 3,1,0,1,0, 4,1,0,0,1, 5,1,1,0,0, 5,2,0,0,1),ncol = 5,byrow = TRUE) d <- list( item1=c(0.2,0.7), item2=c(0.1,0.6), item3=c(0.2,0.6), item4=c(0.2,0.7), item5=c(0.1,0.8)) set.seed(123) sim <- simGDINA(N=1000,Q = Q, delta.parm = d, model = c("MSDINA","MSDINA","DINA", "DINA","DINA","MSDINA","MSDINA")) # simulated data dat <- extract(sim,what = "dat") # estimation # MSDINA need to be specified for each strategy est <- GDINA(dat,Q,model = c("MSDINA","MSDINA","DINA", "DINA","DINA","MSDINA","MSDINA"), control = list(conv.type = "neg2LL",conv.crit = .01)) # Approximate the MS-DINA model using GMS DINA model est2 <- GMSCDM(dat, Q, model = "rDINA", s = 10, control = list(conv.type = "neg2LL",conv.crit = .01)) }