plotly_post.beta: Visualization of Posterior Regression Coefficients in...

In mixtools: Tools for Analyzing Finite Mixture Models

Visualization of Posterior Regression Coefficients in Mixtures of Random Effects Regressions using plotly

Description

Returns a 2x2 matrix of plots summarizing the posterior intercept and slope terms in a mixture of random effects regression with arbitrarily many components.

Usage

plotly_post.beta(y, x, p.beta, p.z,
                 cex = 6,lwd=1,
                 title.size = 15,
                 xlab.size = 15 , xtick.size = 15,
                 ylab.size = 15 , ytick.size = 15,
                 col.data = "#1f77b4",
                 col.comp = NULL)

Arguments

y	A list of N response trajectories with (possibly) varying dimensions of length n_i.
x	A list of N predictor values of dimension n_i. Each trajectory in y has its own design vector.
p.beta	A list of N 2xk matrices giving the posterior intercept and slope values from the output of an EM algorithm.
p.z	An Nxk matrix of posterior membership probabilities from the output of an EM algorithm.
cex	Size of dots of posterior Coefficients.
lwd	Width of lines.
title.size	Size of the main title.
xlab.size	Size of the lable of X-axis.
xtick.size	Size of tick lables of X-axis.
ylab.size	Size of the lable of Y-axis.
ytick.size	Size of tick lables of Y-axis.
col.data	Color of original data points.
col.comp	Color of points and lines of components. Number of colors specified needs to be consistent with number of components.

Details

This is primarily used for within plot.mixEM.

Value

Plots returned.

References

Young, D. S. and Hunter, D. R. (2015) Random Effects Regression Mixtures for Analyzing Infant Habituation, Journal of Applied Statistics, 42(7), 1421–1441.

See Also

regmixEM.mixed, plot.mixEM, post.beta

Examples

data(RanEffdata)
set.seed(100)
x <- lapply(1:length(RanEffdata), function(i)
  matrix(RanEffdata[[i]][, 2:3], ncol = 2))
x <- x[1:20]
y <- lapply(1:length(RanEffdata), function(i)
  matrix(RanEffdata[[i]][, 1], ncol = 1))
y <- y[1:20]
lambda <- c(0.45, 0.55)
mu <- matrix(c(0, 4, 100, 12), 2, 2)
sigma <- 2
R <- list(diag(1, 2), diag(1, 2))
em.out <- regmixEM.mixed(y, x, sigma = sigma, arb.sigma = FALSE,
                         lambda = lambda, mu = mu, R = R,
                         addintercept.random = FALSE,
                         epsilon = 1e-02, verb = TRUE)

x.1 = em.out$x
n = sum(sapply(x.1, nrow))
x.1.sum = sum(sapply(1:length(x.1), function(i) length(x.1[[i]][,1])))
if (x.1.sum == n) {
  x = lapply(1:length(x.1), function(i) matrix(x.1[[i]][,-1], ncol = 1))
} else {
  x = x.1
}

plotly_post.beta(x = x, y = em.out$y, p.beta = em.out$posterior.beta, 
                 p.z = em.out$posterior.z)

mixtools documentation built on Dec. 5, 2022, 5:23 p.m.