plotSEMM (version 2.4)

plotSEMM_probability: Probability plot

Description

Requires plotSEMM_setup be run first. Generates a plot which expresses the mixing probabilities for each latent class conditioned on the latent predictor.

Usage

plotSEMM_probability(SEMLIdatapks, EtaName = "Eta1", lnty = 3, lncol = 1,
  title = "", leg = TRUE, cex = 1.5, ...)

Arguments

SEMLIdatapks

object returned from plotSEMM_setup

EtaName

Label of the latent predictor. If no value is provided, defaults to Eta1.

lnty

Determines the line types used for the class lines. If no value is provided, defaults to 3. See par for information about line type.

lncol

Determines the line colors used for the class lines. If no value is provided, defaults to 1. See par for information about line type.

title

Titles the graph.

leg

Logical variable. If TRUE, a legend accompanies the graph. If FALSE, no legend appears. Defaults to TRUE.

cex

par(cex) value. Default is 1.5

...

addition inputs, mostly from plotSEMM_GUI()

References

Pek, J. & Chalmers, R. P. (2015). Diagnosing Nonlinearity With Confidence Envelopes for a Semiparametric Approach to Modeling Bivariate Nonlinear Relations Among Latent Variables. Structural Equation Modeling, 22, 288-293. 10.1080/10705511.2014.937790

Pek, J., Chalmers, R. P., Kok B. E., & Losardo, D. (2015). Visualizing Confidence Bands for Semiparametrically Estimated Nonlinear Relations among Latent Variables. Journal of Educational and Behavioral Statistics, 40, 402-423. 10.3102/1076998615589129

See Also

plotSEMM_setup, plotSEMM_contour

Examples

# 2 class empirical example on positive emotions and heuristic processing in
# Pek, Sterba, Kok & Bauer (2009)
pi <- c(0.602, 0.398)

alpha1 <- c(3.529, 2.317)

alpha2 <- c(0.02, 0.336)

beta21 <- c(0.152, 0.053)

psi11 <- c(0.265, 0.265)

psi22 <- c(0.023, 0.023)


plotobj <- plotSEMM_setup(pi, alpha1, alpha2, beta21, psi11, psi22)

plotSEMM_probability(plotobj)

plotSEMM_probability(plotobj , EtaName = "Latent Predictor", lnty = 2, title = "Probability")