Commit 1adae530 by Eric Coissac

renames corls.partial to corls_partial

parent 6e42ea2c
......@@ -24,7 +24,7 @@ S3method(subset,procmod_frame)
export(as_procmod_frame)
export(bicenter)
export(corls)
export(corls.partial)
export(corls_partial)
export(corls_test)
export(is.euclid)
export(is_procmod_frame)
......
......@@ -50,7 +50,7 @@ registerDoParallel(1)
#' Procrustean Correlation, and Variance / Covariance Matrices.
#'
#' \code{varls}, \code{corls}, \code{corls.partial} compute the procrustean
#' \code{varls}, \code{corls}, \code{corls_partial} compute the procrustean
#' variance / covariance, correlation, or partial correlation matrices
#' between a set of real matrices and \code{\link[stats]{dist}} objects.
#'
......@@ -143,7 +143,7 @@ registerDoParallel(1)
#' @name varls
#' @aliases varls
#' @aliases corls
#' @aliases corls.partial
#' @aliases corls_partial
#' @export
varls <- function(...,
nrand = 100,
......@@ -284,7 +284,7 @@ corls <- function(..., nrand = 100,
#' @rdname varls
#' @export
corls.partial <- function(..., nrand = 100) {
corls_partial <- function(..., nrand = 100) {
rls <- corls(..., nrand = nrand)
C <- solve(rls)
S <- sqrt(diag(C))
......
......@@ -3,14 +3,14 @@
\name{varls}
\alias{varls}
\alias{corls}
\alias{corls.partial}
\alias{corls_partial}
\title{Procrustean Correlation, and Variance / Covariance Matrices.}
\usage{
varls(..., nrand = 100, p.adjust.method = "holm")
corls(..., nrand = 100, p.adjust.method = "holm")
corls.partial(..., nrand = 100)
corls_partial(..., nrand = 100)
}
\arguments{
\item{...}{the set of matrices or a \code{\link[ProcMod]{procmod_frame}}
......@@ -52,7 +52,7 @@ a \code{procmod_varls} object which corresponds to a numeric
method specified by the \code{p.adjust.method} parameter.
}
\description{
\code{varls}, \code{corls}, \code{corls.partial} compute the procrustean
\code{varls}, \code{corls}, \code{corls_partial} compute the procrustean
variance / covariance, correlation, or partial correlation matrices
between a set of real matrices and \code{\link[stats]{dist}} objects.
}
......
......@@ -709,8 +709,8 @@ if (compute) {
equal.var = TRUE)
partial.data = procmod_frame(A=A,B=B,C=C,D=D)
partial_r2_sims[k, , ,1] <- corls.partial(partial.data,nrand = n_rand)
partial_r2_sims[k, , ,2] <- corls.partial(partial.data,nrand = 0)
partial_r2_sims[k, , ,1] <- corls_partial(partial.data,nrand = n_rand)
partial_r2_sims[k, , ,2] <- corls_partial(partial.data,nrand = 0)
res <- partial_r2_sims[k, , , ]
......
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......@@ -373,7 +373,7 @@ To evaluate relative the power of the three considered tests, pairs of to random
\begin{table}[!t]
\processtable{Estimation of $\overline{\rcovls(\X,\Y)}$ according to the number of random matrices (k) aligned.\label{tab:mrcovls}}{
% latex table generated in R 3.5.2 by xtable 1.8-4 package
% Tue Oct 1 08:44:51 2019
% Tue Oct 1 15:28:19 2019
\begin{tabular}{rrrrrrr}
\hline
& & \multicolumn{2}{c}{normal} & & \multicolumn{2}{c}{exponential}\\ \cline{3-4} \cline{6-7}p & k &\multicolumn{1}{c}{mean} & \multicolumn{1}{c}{sd} & \multicolumn{1}{c}{ } &\multicolumn{1}{c}{mean} & \multicolumn{1}{c}{sd}\\\hline\multirow{3}{*}{10} & 10 & 0.5746 & $1.3687 \times 10^{-2}$ & & 0.5705 & $1.1714 \times 10^{-2}$ \\
......@@ -475,7 +475,7 @@ whatever the $p$ tested (Table~\ref{tab:alpha_pvalue}). This ensure that the pro
of the distribution of $P_{values}$ correlation test to $\mathcal{U}(0,1)$
under the null hypothesis.\label{tab:alpha_pvalue}} {
% latex table generated in R 3.5.2 by xtable 1.8-4 package
% Tue Oct 1 08:44:54 2019
% Tue Oct 1 15:28:22 2019
\begin{tabular*}{0.98\linewidth}{@{\extracolsep{\fill}}crrr}
\hline
& \multicolumn{3}{c}{Cramer-Von Mises p.value} \\
......@@ -497,7 +497,7 @@ Power of the $CovLs$ test based on the estimation of $\overline{RCovLs(X,Y)}$ is
\begin{table}[!t]
\processtable{Power estimation of the procruste tests for two low level of shared variations $5\%$ and $10\%$.\label{tab:power}} {
% latex table generated in R 3.5.2 by xtable 1.8-4 package
% Tue Oct 1 08:44:54 2019
% Tue Oct 1 15:28:22 2019
\begin{tabular}{lcrrrrrrrrr}
\hline
& $R^2$ & \multicolumn{4}{c}{5\%} & &\multicolumn{4}{c}{10\%} \\
......
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