plotSEMM (version 2.4)

plotSEMM_contour: Nonlinear regression function

Description

Requires plotSEMM_setup be run first. Generates (a) the potential nonlinear regression function; (b) bivariate distribution of the latent variables; (c) marginal distributions of the latent variables; (d) within class linear regression functions; and (e) within class marginal distributions for the latent variables.

Usage

plotSEMM_contour(SEMLIdatapks, EtaN2 = "Eta2", EtaN1 = "Eta1",
  classinfo = TRUE, lnty = 3, lncol = 1, title = "", leg = TRUE,
  cex = 1.5, ...)

Arguments

SEMLIdatapks

object returned from plotSEMM_setup

EtaN2

Label for the X axis. If no value is provided, defaults to "Eta2."

EtaN1

Label for the Y axis. If no value is provided, defaults to "Eta1."

classinfo

Logical variable. TRUE shows the lines for each class as well as the combined estimate. FALSE shows only the combined estimate. If no value is provided, defaults to TRUE.

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

Examples

## code for latent variables with two classes
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_contour(plotobj)

plotSEMM_contour(plotobj, EtaN1 = "Latent Predictor", 
   EtaN2 = "Latent Outcome", classinfo = FALSE, lncol = 5)