lmmpar v0.1.0

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Parallel Linear Mixed Model

Embarrassingly Parallel Linear Mixed Model calculations spread across local cores which repeat until convergence.

lmmpar

The goal of lmmpar is to ...

Installation

You can install lmmpar from github with:

# install.packages("devtools")
devtools::install_github("fulyagokalp/lmmpar")


Example

This is a basic example which shows you how to solve a common problem:

# Set up fake data
n <- 10000  # number of subjects
m <- 4      # number of repeats
N <- n * m  # true size of data
p <- 50     # number of betas
q <- 2      # width of random effects

# Initial parameters
# beta has a 1 for the first value.  all other values are ~N(10, 1)
beta <- rbind(1, matrix(rnorm(p, 10), p, 1))
R <- diag(m)
D <- matrix(c(16, 0, 0, 0.025), nrow = q)
sigma <- 1

# Set up data
subject <- rep(1:n, each = m)
repeats <- rep(1:m, n)

subj_x <- lapply(1:n, function(i) cbind(1, matrix(rnorm(m * p), nrow = m)))
X <- do.call(rbind, subj_x)
Z <- X[, 1:q]
subj_beta <- lapply(1:n, function(i) mnormt::rmnorm(1, rep(0, q), D))
subj_err <- lapply(1:n, function(i) mnormt::rmnorm(1, rep(0, m), sigma * R))

# create a known response
subj_y <- lapply(
seq_len(n),
function(i) {
(subj_x[[i]] %*% beta) +
(subj_x[[i]][, 1:q] %*% subj_beta[[i]]) +
subj_err[[i]]
}
)
Y <- do.call(rbind, subj_y)

# run the algorithm in parallel to recover the known betas
ans <- lmmpar(
Y,
X,
Z,
subject,
beta = beta,
R = R,
D = D,
cores = 4,
sigma = sigma,
verbose = TRUE
)


Details

 Encoding UTF-8 License MIT + file LICENSE LazyData TRUE RoxygenNote 6.0.1 URL https://github.com/fulyagokalp/lmmpar BugReports https://github.com/fulyagokalp/lmmpar/issues NeedsCompilation no Packaged 2017-08-02 17:56:25 UTC; barret Repository CRAN Date/Publication 2017-08-03 15:17:37 UTC
 imports bigmemory , doParallel , MASS , matrixcalc , mnormt , plyr depends R (>= 3.2.2) suggests testthat Contributors Barret Schloerke