plot.stepLCplfm: Plot fit of 'stepLCplfm' objects

In plfm: Probabilistic Latent Feature Analysis

Plot fit of stepLCplfm objects

Description

Plot method to visualize the fit of latent class probabilistic feature models with different numbers of features/classes.

Usage

## S3 method for class 'stepLCplfm'
plot(x,which="BIC",...)

Arguments

x	List of latent class probabilistic latent feature analysis objects returned by stepLCplfm.
which	Fit criterion for which models with different numbers of features are compared. The argument which can take the following values: "AIC", "BIC", "Deviance", "Correlation", "VAF"
...	Further arguments are ignored.

Examples

## Not run: 
# example 1: analysis on determinants of anger-related behavior

# load anger data
data(anger)

# compute 5 runs of disjunctive latent class probabilistic feature models
# with 1 up to 3 features and with 1 up to 2 latent classes
# assume constant situation classification per person 
# and class-specific situation parameters (i.e. model=1) 

anger.lst<-stepLCplfm(minF=1,maxF=3,minT=1,maxT=2,data=anger$data,
                      maprule="disj",M=5,emcrit1=1e-3,emcrit2=1e-8,model=1)


# visualize BIC of fitted models 

par(pty="s")
plot(anger.lst)

# print overview fit measures for all estimated models

anger.lst

# print model with 3 features and 1 latent class

anger.lst[[3,1]]

## End(Not run)

## Not run: 
# example 2:Perceptual analysis of associations between car models and car attributes

# load car data
data(car)


# compute 5 runs of disjunctive models with 4 features and 1 up to 3 latent classes
# assume constant attribute classification per respondent 
# and class-specific car parameters (i.e. model 4)

car.lst<-stepLCplfm(minF=4,maxF=4,minT=1,maxT=3,data=car$data3w,
                      maprule="disj",M=5,emcrit1=1e-3,emcrit2=1e-8,model=4,printrun=TRUE)


# visualize BIC of fitted models
plot(car.lst)

# print overview of fitmeasures for all fitted models
car.lst

## End(Not run)

plfm documentation built on March 30, 2022, 5:08 p.m.