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mlmatrixt.R
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mlmatrixt.R
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# mlmatrixt.R
# MixMatrix: Classification with Matrix Variate Normal and t distributions
# Copyright (C) 2018-9 GZ Thompson <gzthompson@gmail.com>
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, a copy is available at
# https://www.R-project.org/Licenses/
#' Maximum likelihood estimation for matrix variate t distributions
#'
#' For the matrix variate normal distribution, maximum likelihood estimates
#' exist for \eqn{N > max(p/q,q/p)+1} and are unique for \eqn{N > max(p,q)}.
#' The number necessary for the matrix variate t has not been worked out but
#' this is a lower bound. This implements an ECME algorithm to estimate the
#' mean, covariance, and degrees of freedom parameters. An AR(1), compound
#' symmetry, or independence restriction can be proposed for either or both
#' variance matrices. However, if they are inappropriate for the data, they may
#' fail with a warning.
#'
#' @param data Either a list of matrices or a 3-D array with matrices in
#' dimensions 1 and 2, indexed by dimension 3.
#' @param row.mean By default, `FALSE`. If `TRUE`, will fit a
#' common mean within each row. If both this and `col.mean` are
#' `TRUE`, there will be a common mean for the entire matrix.
#' @param col.mean By default, `FALSE`. If `TRUE`, will fit a
#' common mean within each row. If both this and `row.mean` are
#' `TRUE`, there will be a common mean for the entire matrix.
#' @param row.variance Imposes a variance structure on the rows. Either
#' 'none', 'AR(1)', 'CS' for 'compound symmetry', 'Correlation' for a
#' correlation matrix, or 'Independence' for
#' independent and identical variance across the rows.
#' Only positive correlations are allowed for AR(1) and CS and these
#' restrictions may not be guaranteed to converge.
#' Note that while maximum likelihood estimators are available (and used)
#' for the unconstrained variance matrices, `optim` is used for any
#' constraints so it may be considerably slower.
#' @param col.variance Imposes a variance structure on the columns.
#' Either 'none', 'AR(1)', 'CS', 'Correlation', or 'Independence'.
#' Only positive correlations are allowed for
#' AR(1) and CS.
#' @param df Starting value for the degrees of freedom. If `fixed = TRUE`,
#' then this is required and not updated. By default, set to 10.
#' @param fixed Whether `df` is estimated or fixed.
#' By default, `TRUE`.
#' @param tol Convergence criterion. Measured against square deviation
#' between iterations of the two variance-covariance matrices.
#' @param max.iter Maximum possible iterations of the algorithm.
#' @param U (optional) Can provide a starting point for the U matrix.
#' By default, an identity matrix.
#' @param V (optional) Can provide a starting point for the V matrix.
#' By default, an identity matrix.
#' @param ... (optional) additional arguments can be passed to `optim`
#' if using restrictions on the variance.
#'
#' @return Returns a list with the following elements:
#' \describe{
#' \item{`mean`}{the mean matrix}
#' \item{`U`}{the between-row covariance matrix}
#' \item{`V`}{the between-column covariance matrix}
#' \item{`var`}{the scalar variance parameter
#' (the first entry of the covariances are restricted to unity)}
#' \item{`nu`}{the degrees of freedom parameter}
#' \item{`iter`}{the number of iterations}
#' \item{`tol`}{the squared difference between iterations of
#' the variance matrices at the time of stopping}
#' \item{`logLik`}{log likelihood of result.}
#' \item{`convergence`}{a convergence flag,
#' `TRUE` if converged.}
#' \item{`call`}{The (matched) function call.}
#' }
#'
#' @export
#' @seealso [rmatrixnorm()], [rmatrixt()],
#' [MLmatrixnorm()]
#'
#' @references
#' Thompson, G Z. R Maitra, W Q Meeker, A Bastawros (2019),
#' "Classification with the matrix-variate-t distribution", arXiv
#' e-prints arXiv:1907.09565 <https://arxiv.org/abs/1907.09565>
#'
#' Dickey, James M. 1967. “Matricvariate Generalizations of the
#' Multivariate t Distribution and the Inverted Multivariate t
#' Distribution.” Ann. Math. Statist. 38 (2): 511–18.
#' \doi{10.1214/aoms/1177698967}
#'
#' Liu, Chuanhai, and Donald B. Rubin. 1994. “The ECME Algorithm:
#' A Simple Extension of EM and ECM with Faster Monotone Convergence.”
#' Biometrika 81 (4): 633–48.
#' \doi{10.2307/2337067}
#'
#' Meng, Xiao-Li, and Donald B. Rubin. 1993. “Maximum Likelihood Estimation
#' via the ECM Algorithm: A General Framework.” Biometrika 80 (2): 267–78.
#' \doi{10.1093/biomet/80.2.267}
#'
#' Rubin, D.B. 1983. “Encyclopedia of Statistical Sciences.” In, 4th ed.,
#' 272–5. John Wiley.
#'
#' @examples
#' set.seed(20180202)
#' # drawing from a distribution with specified mean and covariance
#' A <- rmatrixt(
#' n = 100, mean = matrix(c(100, 0, -100, 0, 25, -1000), nrow = 2),
#' L = matrix(c(2, 1, 0, .1), nrow = 2), list = TRUE, df = 5
#' )
#' # fitting maximum likelihood estimates
#' results <- MLmatrixt(A, tol = 1e-5, df = 5)
#' print(results)
MLmatrixt <- function(data, row.mean = FALSE, col.mean = FALSE,
row.variance = "none", col.variance = "none",
df = 10, fixed = TRUE,
tol = .Machine$double.eps^0.5, max.iter = 5000, U, V,
...) {
if (is.null(df) || df == 0 || is.infinite(df)) {
return(MLmatrixnorm(
data, row.mean, col.mean, row.variance,
col.variance, tol, max.iter, U, V, ...
))
}
if (class(data) == "list") {
data <- array(unlist(data),
dim = c(
nrow(data[[1]]),
ncol(data[[1]]), length(data)
)
)
}
if (!all(
is.numeric(data), is.numeric(tol),
is.numeric(max.iter)
)) {
stop("Non-numeric input. ")
}
if (!(missing(U))) {
if (!(is.numeric(U))) stop("Non-numeric input.")
}
if (!(missing(V))) {
if (!(is.numeric(V))) stop("Non-numeric input.")
}
row_set_var <- FALSE
rowvarparse <- .varparse(row.variance)
row_set_var <- rowvarparse$varflag
row.variance <- rowvarparse$varopt
col_set_var <- FALSE
colvarparse <- .varparse(col.variance)
col_set_var <- colvarparse$varflag
col.variance <- colvarparse$varopt
dims <- dim(data)
if (max(dims[1] / dims[2], dims[2] / dims[1]) > (dims[3] - 1)) {
stop("Need more observations to estimate parameters.")
}
# don't have initial starting point for U and V, start with diag.
if (missing(U)) {
est_u <- diag(dims[1])
} else {
est_u <- U
}
if (missing(V)) {
est_v <- diag(dims[2])
} else {
est_v <- V
}
mu <- rowMeans(data, dims = 2)
if (row.mean) {
# make it so that the mean is constant within a row
mu <- matrix(rowMeans(mu), nrow = dims[1], ncol = dims[2])
}
if (col.mean) {
# make it so that the mean is constant within a column
mu <- matrix(colMeans(mu), nrow = dims[1], ncol = dims[2], byrow = TRUE)
}
# if both are true, this makes it so the mean is constant all over
swept_data <- sweep(data, c(1, 2), mu)
iter <- 0
error_term <- 1e+40
if (col_set_var) { #set var, est_v, swept_data, dims, col_var
if (est_v[1, 2] > 0) {
rho_col <- est_v[1, 2]
} else {
inter_v <- txax(swept_data, 0.5 * (est_u + t(est_u)))
est_v <- rowSums(inter_v, dims = 2) / (dims[3] * dims[1])
if (col.variance == "AR(1)") {
est_v <- stats::cov2cor(est_v)
rho_col <- est_v[1, 2]
}
if (col.variance == "CS") {
est_v <- stats::cov2cor(est_v)
rho_col <- mean(est_v[1, ] / est_v[1, 1])
}
if (col.variance == "I") rho_col <- 0
if (rho_col > .9) rho_col <- .9
if (rho_col < 0) rho_col <- 0
est_v <- varmatgenerate(dims[2], rho_col, col.variance)
}
}
if (row_set_var) {
if (est_u[1, 2] > 0) {
rho_row <- est_u[1, 2]
} else {
inter_u <- xatx(swept_data, 0.5 * (est_v + t(est_v)))
est_u <- rowSums(inter_u, dims = 2) / (dims[3] * dims[2])
if (row.variance == "AR(1)") {
est_u <- stats::cov2cor(est_u)
rho_row <- est_u[1, 2] / est_u[1, 1]
}
if (row.variance == "CS") {
est_u <- stats::cov2cor(est_u)
rho_row <- mean(est_u[1, ] / est_u[1, 1])
}
if (row.variance == "I") rho_row <- 0
if (rho_row > .9) rho_row <- .9
if (rho_row < 0) rho_row <- 0
est_u <- varmatgenerate(dims[1], rho_row, row.variance)
}
}
varflag <- FALSE
p <- dims[1]
q <- dims[2]
n <- dims[3]
while (iter < max.iter && error_term > tol && (!varflag)) {
### E step
s_list <- .sstep(data, mu, est_u, est_v, rep(1.0, n))
ss <- s_list$ss
ssx <- s_list$ssx
ssxx <- s_list$ssxx
ssd <- s_list$ssd
### CM STEP
### MEANS:
new_mu <- .means_function(data,
v = est_v, ss, ssx, rep(1.0, n),
row.mean, col.mean, "t"
)
### VARS:
colvarlist <- .col_vars(
data, new_mu, df, rep(1.0, n), ss, ssx, ssxx,
col.variance, col_set_var, varflag
)
varflag <- colvarlist$varflag
if (!varflag) new_v <- colvarlist$V
rowvarlist <- .row_vars(
data, new_mu, df, rep(1, n), ss, ssx, ssxx,
row.variance, row_set_var, varflag
)
varflag <- rowvarlist$varflag
if (!varflag) new_u <- rowvarlist$U
### IF NU UPDATE
if (!fixed) {
new_df <- df
## insert E step for NU and M step for NU
ssdtmp <- ssd
detss <- determinant(ss, logarithm = TRUE)$modulus[1]
nu_ll <- function(nu) {
(CholWishart::mvdigamma((nu + p - 1) / 2, p) -
CholWishart::mvdigamma((nu + p + q - 1) / 2, p) -
(ssdtmp / n - (detss - p * log(n * (nu + p - 1)) +
p * log(nu + p + q - 1))))
# this latest ECME-ish one gives SLIGHTLY different results but is faster
# (ssdtmp/n + determinant(new_u, logarithm = TRUE)$modulus[1]))
}
if (!isTRUE(sign(nu_ll(1e-6)) * sign(nu_ll(1000)) <= 0)) {
warning("Endpoints of derivative of df likelihood do not have
opposite sign.
Check df specification.")
varflag <- TRUE
} else {
fit0 <- stats::uniroot(nu_ll, c(1e-6, 1000), ...)
new_df <- fit0$root
}
} else {
new_df <- df
}
### CHECK CONVERGENCE
error_term <- sum((new_v - est_v)^2) / (q * q) +
sum((new_u - est_u)^2) / (p * p) +
sum((new_mu - mu)^2) / (p * q) + (df - new_df)^2 / (n * p * q)
### check, force symmetry
if (max(abs(new_v - t(new_v)) > tol)) {
warning("V matrix may not be symmetric")
}
if (max(abs(new_u - t(new_u)) > tol)) {
warning("U matrix may not be symmetric")
}
est_v <- .5 * (new_v + t(new_v))
est_u <- .5 * (new_u + t(new_u))
mu <- new_mu
df <- new_df
iter <- iter + 1
}
if (iter >= max.iter || error_term > tol || varflag) {
warning("Failed to converge")
}
log_lik <- sum(dmatrixt(data, mu, U = est_u, V = est_v, df = df, log = TRUE))
converged <- !(iter >= max.iter || error_term > tol || varflag)
return(list(
mean = mu,
U = est_u / est_u[1, 1],
V = est_v,
var = est_u[1, 1],
nu = df,
iter = iter,
tol = error_term,
logLik = log_lik,
convergence = converged,
call = match.call()
))
}