Estimating multiple-strategy cognitive diagnosis models
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() )
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 |
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 |
an object of class GMSCDM with the following components:
A matrix of success probabilities for each latent class on each item (IRF)
A list of delta parameters
A list of estimated attribute profiles including EAP, MLE and MAP estimates.
A list of test fit statistics including deviance, number of parameters, AIC and BIC
strategy-specific item response function
Probability of adopting each strategy on each item for each latent class
Strategy pravelence
Wenchao Ma, The University of Alabama, wenchao.ma@ua.edu
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.
## Not run:
##################
#
# 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))
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