gom.jml {sirt} | R Documentation |
Grade of Membership Model (Joint Maximum Likelihood Estimation)
Description
This function estimates the grade of membership model employing a joint maximum likelihood estimation method (Erosheva, 2002; p. 23ff.).
Usage
gom.jml(dat, K=2, seed=NULL, globconv=0.001, maxdevchange=0.001,
maxiter=600, min.lambda=0.001, min.g=0.001)
Arguments
dat |
Data frame of dichotomous item responses |
K |
Number of classes |
seed |
Seed value of random number generator. Deterministic starting values
are used for the default value |
globconv |
Global parameter convergence criterion |
maxdevchange |
Maximum change in relative deviance |
maxiter |
Maximum number of iterations |
min.lambda |
Minimum |
min.g |
Minimum |
Details
The item response model of the grade of membership model with classes
for dichotomous correct responses
of person
on item
is
Value
A list with following entries:
lambda |
Data frame of item parameters |
g |
Data frame of individual membership scores |
g.mean |
Mean membership scores |
gcut |
Discretized membership scores |
gcut.distr |
Distribution of discretized membership scores |
K |
Number of classes |
deviance |
Deviance |
ic |
Information criteria |
N |
Number of students |
score |
Person score |
iter |
Number of iterations |
datproc |
List with processed data (recoded data, starting values, ...) |
... |
Further values |
References
Erosheva, E. A. (2002). Grade of membership and latent structure models with application to disability survey data. PhD thesis, Carnegie Mellon University, Department of Statistics.
See Also
S3 method summary.gom
Examples
#############################################################################
# EXAMPLE 1: TIMSS data
#############################################################################
data( data.timss)
dat <- data.timss$data[, grep("M", colnames(data.timss$data) ) ]
# 2 Classes (deterministic starting values)
m2 <- sirt::gom.jml(dat,K=2, maxiter=10 )
summary(m2)
## Not run:
# 3 Classes with fixed seed and maximum number of iterations
m3 <- sirt::gom.jml(dat,K=3, maxiter=50,seed=89)
summary(m3)
## End(Not run)