Summary of Posterior Regression Coefficients in Mixtures of Random Effects Regressions
Returns a 2x2 matrix of plots summarizing the posterior intercept and slope terms in a mixture of random effects regression with arbitrarily many components.
post.beta(y, x, p.beta, p.z)
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. |
This is primarily used for within plot.mixEM.
post.beta returns a 2x2 matrix of plots giving:
(1, 1) |
The data plotted on the x-y axes with all posterior regression lines. |
(1, 2) |
The data plotted on the x-y axes with most probable posterior regression lines. |
(2, 1) |
A beta-space plot of all posterior regression coefficients. |
(1, 1) |
A beta-space plot of most probable posterior regression coefficients. |
Young, D. S. and Hunter, D. R. (2015) Random Effects Regression Mixtures for Analyzing Infant Habituation, Journal of Applied Statistics, 42(7), 1421–1441.
## EM output for simulated data from 2-component mixture of random effects.
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)
## Obtaining the 2x2 matrix of plots.
x.ran <- lapply(1:length(x), function(i) x[[i]][, 2])
p.beta <- em.out$posterior.beta
p.z <- em.out$posterior.z
post.beta(y, x.ran, p.beta = p.beta, p.z = p.z)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.