R: EM Algorithm for Mixtures of Regressions with Random Effects

regmixEM.mixed {mixtools} | R Documentation

## EM Algorithm for Mixtures of Regressions with Random Effects

### Description

Returns EM algorithm output for mixtures of multiple regressions with random effects and an option to incorporate fixed effects and/or AR(1) errors.

### Usage

    regmixEM.mixed(y, x, w = NULL, sigma = NULL, arb.sigma = TRUE,
                   alpha = NULL, lambda = NULL, mu = NULL,
                   rho = NULL, R = NULL, arb.R = TRUE, k = 2,
                   ar.1 = FALSE, addintercept.fixed = FALSE,
                   addintercept.random = TRUE, epsilon = 1e-08,
                   maxit = 10000, verb = FALSE)

### Arguments

`y` | A list of N response trajectories with (possibly) varying dimensions of length n_i.
`x` | A list of N design matrices of dimensions (n_i) x p. Each trajectory in y has its own design matrix.
`w` | A list of N known explanatory variables having dimensions (n-1) x q. If `mixed` = FALSE, then `w` is replaced by a list of N zeros.
`sigma` | A vector of standard deviations. If NULL, then 1/s^2 has random standard exponential entries according to a binning method done on the data.
`arb.sigma` | If TRUE, then `sigma` is k-dimensional. Else a common standard deviation is assumed.
`alpha` | A q-vector of unknown regression parameters for the fixed effects. If NULL and `mixed` = TRUE, then `alpha` is random from a normal distribution with mean and variance according to a binning method done on the data. If `mixed` = FALSE, then `alpha` = 0.
`lambda` | Initial value of mixing proportions for the assumed mixture structure on the regression coefficients. Entries should sum to 1. This determines number of components. If NULL, then `lambda` is random from uniform Dirichlet and the number of components is determined by `mu`.
`mu` | A pxk matrix of the mean for the mixture components of the random regression coefficients. If NULL, then the columns of `mu` are random from a multivariate normal distribution with mean and variance determined by a binning method done on the data.
`rho` | An Nxk matrix giving initial values for the correlation term in an AR(1) process. If NULL, then these values are simulated from a uniform distribution on the interval (-1, 1).
`R` | A list of N pxp covariance matrices for the mixture components of the random regression coefficients. If NULL, then each matrix is random from a standard Wishart distribution according to a binning method done on the data.
`arb.R` | If TRUE, then `R` is a list of N pxp covariance matrices. Else, one common covariance matrix is assumed.
`k` | Number of components. Ignored unless `lambda` is NULL.
`ar.1` | If TRUE, then an AR(1) process on the error terms is included. The default is FALSE.
`addintercept.fixed` | If TRUE, a column of ones is appended to the matrices in w.
`addintercept.random` | If TRUE, a column of ones is appended to the matrices in x before p is calculated.
`epsilon` | The convergence criterion.
`maxit` | The maximum number of iterations.
`verb` | If TRUE, then various updates are printed during each iteration of the algorithm.

### Value

`regmixEM` returns a list of class `mixEM` with items:

`x` | The predictor values corresponding to the random effects.
`y` | The response values.
`w` | The predictor values corresponding to the (optional) fixed effects.
`lambda` | The final mixing proportions.
`mu` | The final mean vectors.
`R` | The final covariance matrices.
`sigma` | The final component error standard deviations.
`alpha` | The final regression coefficients for the fixed effects.
`rho` | The final error correlation values if an AR(1) process is included.
`loglik` | The final log-likelihood.
`posterior.z` | An Nxk matrix of posterior membership probabilities.
`posterior.beta` | A list of N pxk matrices giving the posterior regression coefficient values.
`all.loglik` | A vector of each iteration's log-likelihood.
`restarts` | The number of times the algorithm restarted due to unacceptable choice of initial values.
`ft` | A character vector giving the name of the function.

### References

Xu, W. and Hedeker, D. (2001) A Random-Effects Mixture Model for Classifying Treatment Response in Longitudinal Clinical Trials, Journal of Biopharmaceutical Statistics, 11(4), 253–273.

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`, `post.beta`

### 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)
    em.out[4:10]

[Package mixtools version 1.2.0 Index]