Commit c4c6c590 by Eric Coissac

Corrections to comply with devtools::check_rhub

parent 74443221
 ... ... @@ -4,7 +4,7 @@ Title: Informative Procrustean Matrix Correlation Version: 1.0.0 Author: Eric Coissac, Christelle Gonindard-Melodelima Maintainer: Eric Coissac Description: Estimates procruste corrected correlation between matrices for removing overfitting effect. Description: Estimates corrected Procrustean correlation between matrices for removing overfitting effect. License: CeCILL-2 Encoding: UTF-8 LazyData: true ... ...
 ... ... @@ -34,8 +34,8 @@ export(pca) export(pcoa) export(procmod_frame) export(protate) export(rmatrix) export(simulate_correlation) export(simulate_matrix) export(varls) import(MASS) import(doParallel) ... ...
 ... ... @@ -21,8 +21,8 @@ NULL #' #' @examples #' # Renerate a random matrix of size 10 x 15 #' m1 <- rmatrix(10, 15) #' m2 <- rmatrix(10, 20) #' m1 <- simulate_matrix(10, 15) #' m2 <- simulate_matrix(10, 20) #' mr <- protate(m1, m2) #' #' @author Christelle Gonindard-Melodelima ... ...
 ... ... @@ -17,14 +17,14 @@ #' @return a numeric matrix of \code{n} rows and \code{p} columns #' #' @examples #' sim1 <- rmatrix(25,10) #' sim1 <- simulate_matrix(25,10) #' class(sim1) #' dim(sim1) #' #' @author Eric Coissac #' @author Christelle Gonindard-Melodelima #' @export rmatrix <- function(n, p, equal_var = TRUE) { simulate_matrix <- function(n, p, equal_var = TRUE) { new <- rnorm(n * p, mean = 0, sd = 1) dim(new) <- c(n, p) ... ... @@ -33,12 +33,10 @@ rmatrix <- function(n, p, equal_var = TRUE) { attributes(new)$scaled:center <- NULL attributes(new)$scaled:scale <- NULL new.sd <- sqrt(sum(new^2) / (n - 1)) new <- new / new.sd return(new) new / new.sd } #' Simulate n points of dimension p correlated with a reference matrix. #' Simulate n points of dimension p correlated to a reference matrix. #' #' Simulates a set of point correlated to another set according to the #' procrustean correlation definition. ... ... @@ -63,10 +61,10 @@ rmatrix <- function(n, p, equal_var = TRUE) { #' @return a numeric matrix of \code{nrow(reference)} rows and \code{p} columns #' #' @examples #' sim1 <- rmatrix(25,10) #' sim1 <- simulate_matrix(15,5) #' class(sim1) #' dim(sim1) #' sim2 <- simulate_correlation(sim1,20,0.8) #' sim2 <- simulate_correlation(sim1,10,0.8) #' corls(sim1, sim2)^2 #' #' @author Eric Coissac ... ... @@ -77,7 +75,7 @@ simulate_correlation <- function(reference, p, r2, equal_var = TRUE) { n <- nrow(reference) maxdim <- max(ncol(reference), p) noise <- rmatrix(n, p, equal_var = equal_var) noise <- simulate_matrix(n, p, equal_var = equal_var) if (maxdim == p && maxdim > ncol(reference)) { # noise is the largest matrix ... ... @@ -103,9 +101,7 @@ simulate_correlation <- function(reference, p, r2, equal_var = TRUE) { new <- scale(new, scale = FALSE) attributes(new)\$scaled:center <- NULL new.sd <- sqrt(sum(new^2) / (n - 1)) new <- new / new.sd return(new) new / new.sd } #rmatrix_tree #simulate_matrix_tree
 ... ... @@ -21,8 +21,8 @@ No scaling and no centrering are done, before computing the SVD. } \examples{ # Renerate a random matrix of size 10 x 15 m1 <- rmatrix(10, 15) m2 <- rmatrix(10, 20) m1 <- simulate_matrix(10, 15) m2 <- simulate_matrix(10, 20) mr <- protate(m1, m2) } ... ...
 ... ... @@ -2,7 +2,7 @@ % Please edit documentation in R/simulate.R \name{simulate_correlation} \alias{simulate_correlation} \title{Simulate n points of dimension p correlated with a reference matrix.} \title{Simulate n points of dimension p correlated to a reference matrix.} \usage{ simulate_correlation(reference, p, r2, equal_var = TRUE) } ... ... @@ -35,10 +35,10 @@ The intensity of the correlation is determined by the \code{r2} parameter. } \examples{ sim1 <- rmatrix(25,10) sim1 <- simulate_matrix(15,5) class(sim1) dim(sim1) sim2 <- simulate_correlation(sim1,20,0.8) sim2 <- simulate_correlation(sim1,10,0.8) corls(sim1, sim2)^2 } ... ...
 % Generated by roxygen2: do not edit by hand % Please edit documentation in R/simulate.R \name{rmatrix} \alias{rmatrix} \name{simulate_matrix} \alias{simulate_matrix} \title{Simulate n points of dimension p.} \usage{ rmatrix(n, p, equal_var = TRUE) simulate_matrix(n, p, equal_var = TRUE) } \arguments{ \item{n}{an \code{int} value indicating the number of observations.} ... ... @@ -28,7 +28,7 @@ Therefore they are expected to be equal to 0 and reflect only the random distribution of the covariance between two random vectors. } \examples{ sim1 <- rmatrix(25,10) sim1 <- simulate_matrix(25,10) class(sim1) dim(sim1) ... ...
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