Description Usage Arguments Value Examples
View source: R/plot.iterations.R
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).
1 | iterationPlot(model.fit)
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model.fit |
Object from fitting nominal or gpcm model to data |
Plots of estimated parameters by iteration
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | 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)
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