plotSEMM_contour: Nonlinear regression function

In philchalmers/plotSEMM: Graphing Nonlinear Relations Among Latent Variables from Structural Equation Mixture Models

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()

Author(s)

Bethany Kok and Phil Chalmers [e-mail]

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. doi: 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. doi: 10.3102/1076998615589129

Examples

## Not run: 
## 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) 

## End(Not run)

philchalmers/plotSEMM documentation built on May 25, 2019, 5:03 a.m.