iterationPlot: Plots estimated parameters by iteration for the gpcm and...

In pleLMA: Pseudo-Likelihood Estimation of Log-Multiplicative Association Models

Description

This is a utility function that plots the estimated item parameters by iterations. The plots can be used to determine how many iterations are required to get close to final values. This functions can be used uni- or multi-dimensional gpcm and models. The number of pages equals the number of items and each page has the plots of marginal effects (left side) and category scale values or alph parameters (right).

Usage

iterationPlot(model.fit)

Arguments

model.fit

Object from fitting nominal or gpcm model to data

Value

Plots of estimated parameters by iteration

Examples

data(dass)
inData <- dass[1:250,c("d1", "d2", "d3", "a1","a2","a3","s1","s2","s3")]
#---   input for uni-dimensional
inTraitAdj  <- matrix(1, nrow=1, ncol=1)
inItemTraitAdj <- matrix(1, nrow=9, ncol=1)

#--- generalized partial credit model
g1 <- ple.lma(inData, model.type="gpcm", inItemTraitAdj, inTraitAdj)
iterationPlot(g1)

#--- nominal response model
n1 <- ple.lma(inData, model.type="nominal", inItemTraitAdj,inTraitAdj)
iterationPlot(n1)

#--- Multidimensional models
 inTraitAdj  <- matrix(1, nrow=3, ncol=3)

dpress <- matrix(c(1,0,0), nrow=3, ncol=3, byrow=TRUE)
anxiety <- matrix(c(0,1,0), nrow=3, ncol=3, byrow=TRUE)
stress <- matrix(c(0,0,1), nrow=3, ncol=3, byrow=TRUE)
das <- list(dpress, anxiety, stress)
inItemTraitAdj <- rbind(das[[1]], das[[2]], das[[3]])

g3 <- ple.lma(inData, model.type="gpcm", inItemTraitAdj, inTraitAdj)
iterationPlot(g3)

n3 <- ple.lma(inData, model.type="nominal", inItemTraitAdj, inTraitAdj)
iterationPlot(n3)

pleLMA documentation built on Oct. 6, 2021, 1:08 a.m.