Writing of the documentation

R/covls.R

@@ -30,7 +30,17 @@ registerDoParallel(1)
 #'
 .Trace <- function(X) sum(diag(X))
 
-
+#' Convert a covariance matrix to a correlation matric
+#'
+#' @param c a square covariance/variance matrix
+#' @return correlation matrix corresponding to \code{c}
+#'
+#' @note Internal function do not use.
+#'
+#' @rdname internal.var2cor
+#' @author Eric Coissac
+#' @author Christelle Gonindard-Melodelima
+#'
 .var2cor <- function(c) {
   v <- sqrt(diag(c))
   vv <- v %o% v
@@ -159,11 +169,6 @@ varls <- function(...,
 
   xs <- ortho(xs)
 
-  # xxs <- as.procmod.frame(mapply(tcrossprod,
-  #   xs,
-  #   SIMPLIFY = FALSE
-  # ))
-
   nx <- length(xs)
   n <- nrow(xs)
   n1 <- n - 1
@@ -173,7 +178,6 @@ varls <- function(...,
   # Computes the standard Covls covariance matrix
   for (i in seq_len(nx))
     for (j in i:nx)
-      #cov_xxs[i, j] <- Re(.Trace(expm::sqrtm(xxs[[i]] %*% xxs[[j]])))
       cov_xxs[i, j] <- sum(svd(crossprod(xs[[i]],xs[[j]]))$d) / n1
 
   cov_xxs <- as.matrix(Matrix::forceSymmetric(cov_xxs, uplo = "U"))
@@ -186,27 +190,6 @@ varls <- function(...,
     for (i in seq_len(nx))
       v_xs[[i]] <- var(xs[[i]])
 
-    # s_cov_xxs <- array(0, dim = c(nx, nx, nrand))
-    # for(k in seq_len(nrand)) {
-    #   #s1_cov_xxs <- matrix(0, nrow = nx, ncol = nx)
-    #   r_xs <- vector(mode = "list", nx)
-    #   r_ys <- vector(mode = "list", nx)
-    #   for (i in seq_len(nx)) {
-    #     r_xs[[i]] <- MASS::mvrnorm(n,
-    #                                rep(0,nrow(v_xs[[i]])),
-    #                                Sigma = v_xs[[i]],
-    #                                empirical = TRUE)
-    #     r_ys[[i]] <- MASS::mvrnorm(n,
-    #                                rep(0,nrow(v_xs[[i]])),
-    #                                Sigma = v_xs[[i]],
-    #                                empirical = TRUE)
-    #   }
-    #
-    #   for (i in seq_len(nx))
-    #     for (j in i:nx)
-    #       s_cov_xxs[i, j, k] <- sum(svd(crossprod(r_xs[[i]],r_ys[[j]]))$d)
-    # }
-
     if (! getDoParRegistered()) registerDoParallel(1)
 
     s_cov_xxs <- foreach(k = seq_len(nrand),
@@ -263,7 +246,6 @@ varls <- function(...,
     colnames(p_values) <- x_names
     rownames(p_values) <- x_names
 
-    #attr(cov_xxs, "scovls") <- s_cov_xxs
     attr(cov_xxs, "rcovls") <- as.matrix(r_cov_xxs)
     attr(cov_xxs, "p.value") <- p_values
     attr(cov_xxs, "nrand") <- nrand

R/multivariate.R

@@ -45,7 +45,7 @@ as.data.frame.dist <- function(x, row.names = NULL, optional = FALSE, ...) {
 #' an eucleadean space using the Kruskal's Non-metric
 #' Multidimensional Scaling. This function is mainly a simplified
 #' interface on the \code{\link[MASS]{isoMDS}} function using as
-#' much as possible dimensionsto limit the stress. The aims of this
+#' much as possible dimensions to limit the stress. The aims of this
 #' NDMS being only to project point in an orthogonal space therefore
 #' without any correlation between axis. Because a non-metric method
 #' is used no condition is required on the used distance.
@@ -58,6 +58,10 @@ as.data.frame.dist <- function(x, row.names = NULL, optional = FALSE, ...) {
 #' @param tol         convergence tolerance.
 #' @param p           Power for Minkowski distance in the configuration space.
 #'
+#' @return a numeric matrix with at most \code{n-1} dimensions, with
+#'         \code{n} the number pf observations. This matrix defines the
+#'         coordinates of each point in the orthogonal space.
+#'
 #' @author Eric Coissac
 #' @author Christelle Gonindard-Melodelima
 #' @export
@@ -105,6 +109,14 @@ nmds <- function(distances,
 #' is used the used distance must be euclidean
 #' (cf \code{\link[ProcMod]{is.euclid}}).
 #'
+#' @param distances   a \code{\link[stats]{dist}} object or a
+#'                    \code{\link[base]{matrix}}
+#'                    object representing a distance matrix.
+#'
+#' @return a numeric matrix with at most \code{n-1} dimensions, with
+#'         \code{n} the number pf observations. This matrix defines the
+#'         coordinates of each point in the orthogonal space.
+#'
 #' @author Eric Coissac
 #' @author Christelle Gonindard-Melodelima
 #' @export
@@ -125,6 +137,23 @@ pcoa <- function(distances) {
 
 #' Project a set of points in a euclidean space (PCA).
 #'
+#' Project a set of points defined by a set of numeric variables in
+#' an eucleadean space using the pricipal componant analysis.
+#' This function is mainly a simplified
+#' interface on the \code{\link[stats]{prcomp}} function using as
+#' much as possible dimensions to keep all the variation. The aims of this
+#' PCA being only to project point in an orthogonal space therefore
+#' without any correlation between axis. Data are centered by not scaled by
+#' default.
+#'
+#' @param data a numeric matrix describing the points
+#' @param scale a \code{logical} value indicating if the dimensions must be scaled
+#'        to force for every column that \code{sd=1}. \code{FALSE} by default.
+#'
+#' @return a numeric matrix with at most \code{n-1} dimensions, with
+#'         \code{n} the number pf observations. This matrix defines the
+#'         coordinates of each point in the orthogonal space.
+#'
 #' @author Eric Coissac
 #' @author Christelle Gonindard-Melodelima
 #' @export
@@ -150,6 +179,8 @@ pca <- function(data, scale = FALSE) {
 #' Inspired from the algorithm described in stackoverflow
 #' \url{https://stackoverflow.com/questions/43639063/double-centering-in-r}
 #'
+#' @author Eric Coissac
+#' @author Christelle Gonindard-Melodelima
 #' @export
 bicenter <- function(m) {
 
@@ -167,6 +198,11 @@ bicenter <- function(m) {
 #' Actually a simplified version of the ADE4 implementation
 #' (\code{\link[ade4]{is.euclid}}).
 #'
+#' @param distances	an object of class 'dist'
+#' @param tol	a tolerance threshold : an eigenvalue is
+#'        considered positive if it is larger than
+#'        -tol*lambda1 where lambda1 is the largest eigenvalue.
+#'
 #' @author Eric Coissac
 #' @author Christelle Gonindard-Melodelima
 #' @export
@@ -204,6 +240,17 @@ is.orthogonal <- function(x) {
 #' When points are described by a set of variable the
 #' \code{\link[ProcMod]{nmds}} is used.
 #'
+#' @param data a numeric matrix describing the points
+#' @param tol	a tolerance threshold : an eigenvalue is
+#'        considered positive if it is larger than
+#'        -tol*lambda1 where lambda1 is the largest eigenvalue.
+#' @param scale a \code{logical} value indicating if the dimensions must be scaled
+#'        to force for every column that \code{sd=1}. \code{FALSE} by default.
+#'
+#' @return a numeric matrix with at most \code{n-1} dimensions, with
+#'         \code{n} the number pf observations. This matrix defines the
+#'         coordinates of each point in the orthogonal space.
+#'
 #' @author Eric Coissac
 #' @author Christelle Gonindard-Melodelima
 #' @export
@@ -211,8 +258,7 @@ ortho <- function(data, ...) {
   UseMethod("ortho", data)
 }
 
-#' @author Eric Coissac
-#' @author Christelle Gonindard-Melodelima
+#' @rdname ortho
 #' @export
 ortho.dist <- function(x, tol = 1e-7) {
   if (ProcMod::is.euclid(x, tol = tol)) {
@@ -222,15 +268,13 @@ ortho.dist <- function(x, tol = 1e-7) {
   ProcMod::nmds(x)
 }
 
-#' @author Eric Coissac
-#' @author Christelle Gonindard-Melodelima
+#' @rdname ortho
 #' @export
 ortho.matrix <- function(x, scale = FALSE) {
   pca(x, scale = scale)
 }
 
-#' @author Eric Coissac
-#' @author Christelle Gonindard-Melodelima
+#' @rdname ortho
 #' @export
 ortho.data.frame <- function(x, scale = FALSE) {
   if (!is.null(attributes(x)$is.dist) &&
@@ -242,8 +286,7 @@ ortho.data.frame <- function(x, scale = FALSE) {
 }
 
 
-#' @author Eric Coissac
-#' @author Christelle Gonindard-Melodelima
+#' @rdname ortho
 #' @export
 ortho.procmod.frame <- function(x) {
   if (is.orthogonal(x)) {

R/procmod_frame.R

@@ -14,6 +14,7 @@ NULL
 #'
 #' @return a new matrix with the same number of columns but with `nrow`
 #'         rows.
+#' @note Internal function do not use.
 #'
 #' @author Eric Coissac <eric.coissac@metabarcoding.org>
 #' @author Christelle Gonindard-Melodelima <christelle.gonindard@metabarcoding.org>
@@ -49,6 +50,7 @@ NULL
 #'               of the returned matrix
 #'
 #' @return a new numeric matrix with correct size.
+#' @note Internal function do not use.
 #'
 #' @author Eric Coissac <eric.coissac@metabarcoding.org>
 #' @author Christelle Gonindard-Melodelima <christelle.gonindard@metabarcoding.org>
@@ -164,6 +166,37 @@ NULL
 
 #' The procmod.frame data structure.
 #'
+#' A \code{procmod.frame} can be considered as the analog of a
+#' \code{data.frame} for vector data. In a \code{procmod.frame}
+#' each element, equivalent to a column in a \code{data.frame}
+#' is a numeric matrix or a distance matrix object (\code{dist}).
+#' Every element must describe the same number of individuals.
+#' Therefore every numeric matrix must have the same number of row
+#' (\code{nrow}) and every distance matrix must have the same size
+#' (\code{attr(d,"Size")}). A \code{procmod.frame} can simultaneously
+#' contain both types of data, numeric and distance matrix.
+#'
+#'
+#' @examples
+#' library(vegan)
+#' data(bacteria)
+#' data(eukaryotes)
+#' data(soil)
+#'
+#' dataset <- procmod.frame(euk = vegdist(decostand(eukaryotes,
+#'                                                  method = "hellinger"),
+#'                                        method = "euclidean"),
+#'                          bac = vegdist(decostand(bacteria,
+#'                                                  method = "hellinger"),
+#'                                        method = "euclidean"),
+#'                          soil = scale(soil,
+#'                                       center = TRUE,
+#'                                       scale  = TRUE))
+#' length(dataset)
+#' nrow(dataset)
+#' ncol(dataset)
+#' dataset$euk
+#'
 #' @author Eric Coissac
 #' @author Christelle Gonindard-Melodelima
 #' @export

R/simulate.R

@@ -3,10 +3,29 @@ NULL
 
 #' Simulate n points of dimension p.
 #'
-#' Points are simulated using the \code{\link[mvtnorm]{rmvnorm}} from
-#' the \code{mvtnorm}. The mean of each dimension is set to 0 and all
-#' the variances to 1 with all the covariances set to 0.
+#' Points are simulated by drawing values of each dimension from a normal
+#' distribution of mean 0 and standard deviation equals to 1.
+#' The mean of each dimension is forced to 0 (data are centred).
+#' By default variable are also scaled to enforce a strandard deviation
+#' strictly equal to 1. Covariances between dimensions are not controled.
+#' Therefore they are expected to be equal to 0 and reflect only the
+#' random distribution of the covariance between two random vectors.
 #'
+#' @param n an \code{int} value indicating the number of observations.
+#' @param p an \code{int} value indicating the number of dimensions (variables)
+#'        simulated
+#' @param equal.var a \code{logical} value indicating if the dimensions must be scaled
+#'        to force \code{sd=1}. \code{TRUE} by default.
+#'
+#' @return a numeric matrix of \code{n} rows and \code{p} columns
+#'
+#' @examples
+#' sim1 <- simulate_matrix(25,10)
+#' class(sim1)
+#' dim(sim1)
+#'
+#' @author Eric Coissac
+#' @author Christelle Gonindard-Melodelima
 #' @export
 simulate_matrix <- function(n, p, equal.var = TRUE) {
   new <- rnorm(n * p, mean = 0, sd = 1)
@@ -24,6 +43,37 @@ simulate_matrix <- function(n, p, equal.var = TRUE) {
 
 #' Simulate n points of dimension p correlated with a reference matrix.
 #'
+#' Simulates a set of point correlated to another set according to the
+#' procrustean correlation definition.
+#' Points are simulated by drawing values of each dimension from a normal
+#' distribution of mean 0 and standard deviation equals to 1.
+#' The mean of each dimension is forced to 0 (data are centred).
+#' By default variable are also scaled to enforce a strandard deviation
+#' strictly equal to 1. Covariances between dimensions are not controled.
+#' Therefore they are expected to be equal to 0 and reflect only the
+#' random distribution of the covariance between two random vectors.
+#' The intensity of the correlation is determined by the \code{r2}
+#' parameter.
+#'
+#' @param reference a numeric matrix to which the simulated data will be correlated
+#' @param p an \code{int} value indicating the number of dimensions (variables)
+#'        simulated
+#' @param r2 the fraction of variation shared between the \code{reference} and the
+#'        simulated data
+#' @param equal.var a \code{logical} value indicating if the dimensions must be scaled
+#'        to force \code{sd=1}. \code{TRUE} by default.
+#'
+#' @return a numeric matrix of \code{nrow(reference)} rows and \code{p} columns
+#'
+#' @examples
+#' sim1 <- simulate_matrix(25,10)
+#' class(sim1)
+#' dim(sim1)
+#' sim2 <- simulate_correlation(sim1,20,0.8)
+#' corls(sim1, sim2)^2
+#'
+#' @author Eric Coissac
+#' @author Christelle Gonindard-Melodelima
 #' @export
 
 simulate_correlation <- function(reference, p, r2, equal.var = TRUE) {

data-raw/data.prep.R

+require(vegan)
+
 bacteria <- read.csv("data-raw/bacteria.csv",
   header = TRUE,
   row.names = 1,
@@ -28,8 +30,53 @@ geography <- read.csv("data-raw/geography.csv",
   na.strings = "NA"
 )
 
-devtools::use_data(bacteria, overwrite = TRUE)
-devtools::use_data(eukaryotes, overwrite = TRUE)
-devtools::use_data(climat, overwrite = TRUE)
-devtools::use_data(soil, overwrite = TRUE)
-devtools::use_data(geography, overwrite = TRUE)
+usethis::use_data(bacteria, overwrite = TRUE)
+usethis::use_data(eukaryotes, overwrite = TRUE)
+usethis::use_data(climat, overwrite = TRUE)
+usethis::use_data(soil, overwrite = TRUE)
+usethis::use_data(geography, overwrite = TRUE)
+
+#
+# MicroCurvulatum datasets
+#
+
+mc.bacteria <- read.table("data-raw/MicroCurvulatum/rarefied_bacterial_community_matrix.txt",
+  row.names = 1,
+  header = 1,
+  na.strings = "NA"
+)
+
+mc.bacteria = as.matrix(mc.bacteria)
+
+mc.fungi <- read.table("data-raw/MicroCurvulatum/rarefied_fungal_community_matrix.txt",
+  row.names = 1,
+  header = 1,
+  na.strings = "NA"
+)
+
+mc.fungi <- as.matrix(mc.fungi[row.names(mc.bacteria),])
+
+mc.plants <- read.table("data-raw/MicroCurvulatum/Phytosocio.txt",
+  row.names = 1,
+  header = 1,
+  na.strings = "NA"
+)
+
+mc.plants <- as.matrix(mc.plants[substr(row.names(mc.bacteria),
+                              1,
+                              nchar(row.names(mc.bacteria))-1),])
+
+mc.soil <- read.table("data-raw/MicroCurvulatum/SOIL_CHEM.txt",
+  row.names = 1,
+  header = 1,
+  na.strings = "NA"
+)
+
+mc.soil <- as.matrix(mc.soil[row.names(mc.bacteria),5:10])
+
+MicroCurvulatum = ProcMod::procmod.frame(bacteria = mc.bacteria,
+                                         fungi = mc.fungi,
+                                         plants = mc.plants,
+                                         soil = mc.soil)
+
+usethis::use_data(MicroCurvulatum, overwrite = TRUE)

man/bicenter.Rd

@@ -13,3 +13,8 @@ colSums and rowSums of the returned matrix are all equal to zero.
 Inspired from the algorithm described in stackoverflow
 \url{https://stackoverflow.com/questions/43639063/double-centering-in-r}
 }
+\author{
+Eric Coissac
+
+Christelle Gonindard-Melodelima
+}

man/internal.procmod_coerce_value.Rd

@@ -19,6 +19,9 @@ a new numeric matrix with correct size.
 Transforme the \code{x} value into a \code{numeric matrix} of
              the correct size or into a \code{dist} object.
 }
+\note{
+Internal function do not use.
+}
 \author{
 Eric Coissac <eric.coissac@metabarcoding.org>
 

man/internal.rep_matrix.Rd

@@ -22,6 +22,9 @@ repeats several times the rows of a matrix
              final row count must be a multiple of the
              initial row count
 }
+\note{
+Internal function do not use.
+}
 \author{
 Eric Coissac <eric.coissac@metabarcoding.org>
 

man/internal.var2cor.Rd

+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/covls.R
+\name{.var2cor}
+\alias{.var2cor}
+\title{Convert a covariance matrix to a correlation matric}
+\usage{
+.var2cor(c)
+}
+\arguments{
+\item{c}{a square covariance/variance matrix}
+}
+\value{
+correlation matrix corresponding to \code{c}
+}
+\description{
+Convert a covariance matrix to a correlation matric
+}
+\note{
+Internal function do not use.
+}
+\author{
+Eric Coissac
+
+Christelle Gonindard-Melodelima
+}

man/is.euclid.Rd

@@ -6,6 +6,13 @@
 \usage{
 is.euclid(distances, tol = 1e-07)
 }
+\arguments{
+\item{distances}{an object of class 'dist'}
+
+\item{tol}{a tolerance threshold : an eigenvalue is
+considered positive if it is larger than
+-tol*lambda1 where lambda1 is the largest eigenvalue.}
+}
 \description{
 Actually a simplified version of the ADE4 implementation
 (\code{\link[ade4]{is.euclid}}).

man/nmds.Rd

@@ -19,12 +19,17 @@ object representing a distance matrix.}
 
 \item{p}{Power for Minkowski distance in the configuration space.}
 }
+\value{
+a numeric matrix with at most \code{n-1} dimensions, with
+        \code{n} the number pf observations. This matrix defines the
+        coordinates of each point in the orthogonal space.
+}
 \description{
 Project a set of points defined by a distance matrix in
 an eucleadean space using the Kruskal's Non-metric
 Multidimensional Scaling. This function is mainly a simplified
 interface on the \code{\link[MASS]{isoMDS}} function using as
-much as possible dimensionsto limit the stress. The aims of this
+much as possible dimensions to limit the stress. The aims of this
 NDMS being only to project point in an orthogonal space therefore
 without any correlation between axis. Because a non-metric method
 is used no condition is required on the used distance.

man/ortho.Rd

@@ -2,9 +2,36 @@
 % Please edit documentation in R/multivariate.R
 \name{ortho}
 \alias{ortho}
+\alias{ortho.dist}
+\alias{ortho.matrix}
+\alias{ortho.data.frame}
+\alias{ortho.procmod.frame}
 \title{Project a dataset in a euclidean space.}
 \usage{
 ortho(data, ...)
+
+\method{ortho}{dist}(x, tol = 1e-07)
+
+\method{ortho}{matrix}(x, scale = FALSE)
+
+\method{ortho}{data.frame}(x, scale = FALSE)
+
+\method{ortho}{procmod.frame}(x)
+}
+\arguments{
+\item{data}{a numeric matrix describing the points}
+
+\item{tol}{a tolerance threshold : an eigenvalue is
+considered positive if it is larger than
+-tol*lambda1 where lambda1 is the largest eigenvalue.}
+
+\item{scale}{a \code{logical} value indicating if the dimensions must be scaled
+to force for every column that \code{sd=1}. \code{FALSE} by default.}
+}
+\value{
+a numeric matrix with at most \code{n-1} dimensions, with
+        \code{n} the number pf observations. This matrix defines the
+        coordinates of each point in the orthogonal space.
 }
 \description{
 Project a set of points defined by a distance matrix

man/pca.Rd

@@ -6,8 +6,26 @@
 \usage{
 pca(data, scale = FALSE)
 }
+\arguments{
+\item{data}{a numeric matrix describing the points}
+
+\item{scale}{a \code{logical} value indicating if the dimensions must be scaled
+to force for every column that \code{sd=1}. \code{FALSE} by default.}
+}
+\value{
+a numeric matrix with at most \code{n-1} dimensions, with
+        \code{n} the number pf observations. This matrix defines the
+        coordinates of each point in the orthogonal space.
+}
 \description{
-Project a set of points in a euclidean space (PCA).
+Project a set of points defined by a set of numeric variables in
+an eucleadean space using the pricipal componant analysis.
+This function is mainly a simplified
+interface on the \code{\link[stats]{prcomp}} function using as
+much as possible dimensions to keep all the variation. The aims of this
+PCA being only to project point in an orthogonal space therefore
+without any correlation between axis. Data are centered by not scaled by
+default.
 }
 \author{
 Eric Coissac

man/pcoa.Rd

@@ -6,6 +6,16 @@
 \usage{
 pcoa(distances)
 }
+\arguments{
+\item{distances}{a \code{\link[stats]{dist}} object or a
+\code{\link[base]{matrix}}
+object representing a distance matrix.}
+}
+\value{
+a numeric matrix with at most \code{n-1} dimensions, with
+        \code{n} the number pf observations. This matrix defines the
+        coordinates of each point in the orthogonal space.
+}
 \description{
 Project a set of points defined by a distance matrix in
 an eucleadean space using the Principal Coordinates Analysis

man/procmod.frame.Rd

@@ -14,11 +14,36 @@ is.procmod.frame(x)
 as.procmod.frame(data, ...)
 }
 \description{
-The procmod.frame data structure.
+A \code{procmod.frame} can be considered as the analog of a
+\code{data.frame} for vector data. In a \code{procmod.frame}
+each element, equivalent to a column in a \code{data.frame}
+is a numeric matrix or a distance matrix object (\code{dist}).
+Every element must describe the same number of individuals.
+Therefore every numeric matrix must have the same number of row
+(\code{nrow}) and every distance matrix must have the same size
+(\code{attr(d,"Size")}). A \code{procmod.frame} can simultaneously
+contain both types of data, numeric and distance matrix.
+}
+\examples{
+library(vegan)
+data(bacteria)
+data(eukaryotes)
+data(soil)
 
-Check if an object is a ProcMod Frame.
+dataset <- procmod.frame(euk = vegdist(decostand(eukaryotes,
+                                                 method = "hellinger"),
+                                       method = "euclidean"),
+                         bac = vegdist(decostand(bacteria,
+                                                 method = "hellinger"),
+                                       method = "euclidean"),
+                         soil = scale(soil,
+                                      center = TRUE,
+                                      scale  = TRUE))
+length(dataset)
+nrow(dataset)
+ncol(dataset)
+dataset$euk
 
-Coerce to a ProcMod Frame.
 }
 \author{
 Eric Coissac

man/simulate_correlation.Rd

@@ -6,6 +6,44 @@
 \usage{
 simulate_correlation(reference, p, r2, equal.var = TRUE)
 }
+\arguments{
+\item{reference}{a numeric matrix to which the simulated data will be correlated}
+
+\item{p}{an \code{int} value indicating the number of dimensions (variables)
+simulated}
+
+\item{r2}{the fraction of variation shared between the \code{reference} and the
+simulated data}
+
+\item{equal.var}{a \code{logical} value indicating if the dimensions must be scaled
+to force \code{sd=1}. \code{TRUE} by default.}
+}
+\value{
+a numeric matrix of \code{nrow(reference)} rows and \code{p} columns
+}
 \description{
-Simulate n points of dimension p correlated with a reference matrix.
+Simulates a set of point correlated to another set according to the
+procrustean correlation definition.
+Points are simulated by drawing values of each dimension from a normal
+distribution of mean 0 and standard deviation equals to 1.
+The mean of each dimension is forced to 0 (data are centred).
+By default variable are also scaled to enforce a strandard deviation
+strictly equal to 1. Covariances between dimensions are not controled.
+Therefore they are expected to be equal to 0 and reflect only the
+random distribution of the covariance between two random vectors.
+The intensity of the correlation is determined by the \code{r2}
+parameter.
+}
+\examples{
+sim1 <- simulate_matrix(25,10)
+class(sim1)
+dim(sim1)
+sim2 <- simulate_correlation(sim1,20,0.8)
+corls(sim1, sim2)^2
+
+}
+\author{
+Eric Coissac
+
+Christelle Gonindard-Melodelima
 }

man/simulate_matrix.Rd

@@ -6,8 +6,35 @@
 \usage{
 simulate_matrix(n, p, equal.var = TRUE)
 }
+\arguments{
+\item{n}{an \code{int} value indicating the number of observations.}
+
+\item{p}{an \code{int} value indicating the number of dimensions (variables)
+simulated}
+
+\item{equal.var}{a \code{logical} value indicating if the dimensions must be scaled
+to force \code{sd=1}. \code{TRUE} by default.}
+}
+\value{
+a numeric matrix of \code{n} rows and \code{p} columns
+}
 \description{
-Points are simulated using the \code{\link[mvtnorm]{rmvnorm}} from
-the \code{mvtnorm}. The mean of each dimension is set to 0 and all
-the variances to 1 with all the covariances set to 0.
+Points are simulated by drawing values of each dimension from a normal
+distribution of mean 0 and standard deviation equals to 1.
+The mean of each dimension is forced to 0 (data are centred).
+By default variable are also scaled to enforce a strandard deviation
+strictly equal to 1. Covariances between dimensions are not controled.
+Therefore they are expected to be equal to 0 and reflect only the
+random distribution of the covariance between two random vectors.
+}
+\examples{
+sim1 <- simulate_matrix(25,10)
+class(sim1)
+dim(sim1)
+
+}
+\author{
+Eric Coissac
+
+Christelle Gonindard-Melodelima
 }