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.

Usage

post.beta(y, x, p.beta, p.z)

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.

Value

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.

See also

regmixEM.mixed, plot.mixEM

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.

Details

This is primarily used for within plot.mixEM.

Examples

  if (FALSE) { # \dontrun{
## 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)
} # }