### 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. } ... ...
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 ... ... @@ -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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