R: Summary of Posterior Regression Coefficients in Mixtures of...

post.beta {mixtools} | R Documentation

## Summary of Posterior Regression Coefficients in Mixtures of Random Effects Regressions

### 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

    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.

### Details

This is primarily used for within `plot.mixEM`.

### 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.

### 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`

### Examples

    ## 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)

[Package mixtools version 1.2.0 Index]