Commit 6de2ec12 by Eric Coissac

Rename parameter equal.var equal_var

parent 48ce57d0
......@@ -11,7 +11,7 @@
#' @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
#' @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
......@@ -24,11 +24,11 @@
#' @author Eric Coissac
#' @author Christelle Gonindard-Melodelima
#' @export
simulate_matrix <- 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)
new <- scale(new, center = TRUE, scale = equal.var)
new <- scale(new, center = TRUE, scale = equal_var)
attributes(new)$`scaled:center` <- NULL
attributes(new)$`scaled:scale` <- NULL
......@@ -57,7 +57,7 @@ simulate_matrix <- function(n, p, equal.var = TRUE) {
#' 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
#' @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
......@@ -73,11 +73,11 @@ simulate_matrix <- function(n, p, equal.var = TRUE) {
#' @author Christelle Gonindard-Melodelima
#' @export
simulate_correlation <- function(reference, p, r2, equal.var = TRUE) {
simulate_correlation <- function(reference, p, r2, equal_var = TRUE) {
n <- nrow(reference)
maxdim <- max(ncol(reference), p)
noise <- simulate_matrix(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
......
......@@ -4,7 +4,7 @@
\alias{simulate_correlation}
\title{Simulate n points of dimension p correlated with a reference matrix.}
\usage{
simulate_correlation(reference, p, r2, equal.var = TRUE)
simulate_correlation(reference, p, r2, equal_var = TRUE)
}
\arguments{
\item{reference}{a numeric matrix to which the simulated data will be correlated}
......@@ -15,7 +15,7 @@ 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
\item{equal_var}{a \code{logical} value indicating if the dimensions must be scaled
to force \code{sd=1}. \code{TRUE} by default.}
}
\value{
......
......@@ -4,7 +4,7 @@
\alias{simulate_matrix}
\title{Simulate n points of dimension p.}
\usage{
simulate_matrix(n, p, equal.var = TRUE)
simulate_matrix(n, p, equal_var = TRUE)
}
\arguments{
\item{n}{an \code{int} value indicating the number of observations.}
......@@ -12,7 +12,7 @@ simulate_matrix(n, p, equal.var = TRUE)
\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
\item{equal_var}{a \code{logical} value indicating if the dimensions must be scaled
to force \code{sd=1}. \code{TRUE} by default.}
}
\value{
......
......@@ -301,8 +301,8 @@ h0_sims = array(0,dim = c(n_sim,length(p_qs),8))
for (k in seq_len(n_sim))
for (i in seq_along(p_qs)) {
X <- simulate_matrix(n_indivdual,p_qs[i],equal.var = TRUE)
Y <- simulate_matrix(n_indivdual,p_qs[i],equal.var = TRUE)
X <- simulate_matrix(n_indivdual,p_qs[i],equal_var = TRUE)
Y <- simulate_matrix(n_indivdual,p_qs[i],equal_var = TRUE)
h0_sims[k,i,1] <- ProcMod::corls(X,Y,nrand = 0)[1,2]
h0_sims[k,i,2] <- ProcMod::corls(X,Y,nrand = n_rand)[1,2]
......@@ -396,7 +396,7 @@ initial_var <- 3
n_indivdual <- 20
supplement_vars <- 0:50
X = simulate_matrix(n = n_indivdual,p = initial_var,equal.var = TRUE)
X = simulate_matrix(n = n_indivdual,p = initial_var,equal_var = TRUE)
Y = simulate_correlation(reference = X, p = initial_var, r2 = 0.4)
h1_sims_over = array(0,dim = c(length(supplement_vars),8))
......@@ -462,11 +462,11 @@ if (compute) {
for (r in seq_along(r2s)) {
X <- simulate_matrix(n_indivduals[i],
p_qs[j],
equal.var = TRUE)
equal_var = TRUE)
Y <- simulate_correlation(X,
p_qs[j],
r2 = r2s[r],
equal.var = TRUE)
equal_var = TRUE)
r2_sims[k,i,j,r,1] <- corls(X,Y,nrand = n_rand)[1,2]
r2_sims[k,i,j,r,2] <- corls(X,Y,nrand = 0)[1,2]
......@@ -566,11 +566,11 @@ for (k in seq_len(n_sim)) {
for (r in seq_along(r2s)) {
X <- simulate_matrix(n_indivduals[i],
1,
equal.var = TRUE)
equal_var = TRUE)
Y <- simulate_correlation(X,
1,
r2 = r2s[r],
equal.var = TRUE)
equal_var = TRUE)
r2_sims_vec[k,i,r,1] <- corls(X,Y,nrand = n_rand)[1,2]^2
r2_sims_vec[k,i,r,2] <- corls(X,Y,nrand = 0)[1,2]^2
......@@ -691,22 +691,22 @@ if (compute) {
} else {
A <- simulate_matrix(n_indivdual,
p_q,
equal.var = TRUE)
equal_var = TRUE)
B <- simulate_correlation(A,
p_q,
r2 = r2_AB,
equal.var = TRUE)
equal_var = TRUE)
C <- simulate_correlation(B,
p_q,
r2 = r2_BC,
equal.var = TRUE)
equal_var = TRUE)
D <- simulate_correlation(C,
p_q,
r2 = r2_CD,
equal.var = TRUE)
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)
......@@ -799,8 +799,8 @@ h0_alpha = array(0,dim = c(n_sim,length(p_qs),3))
for (k in seq_len(n_sim))
for (i in seq_along(p_qs)) {
X <- simulate_matrix(n_indivdual,p_qs[i],equal.var = TRUE)
Y <- simulate_matrix(n_indivdual,p_qs[i],equal.var = TRUE)
X <- simulate_matrix(n_indivdual,p_qs[i],equal_var = TRUE)
Y <- simulate_matrix(n_indivdual,p_qs[i],equal_var = TRUE)
h0_alpha[k,i,1] <- attr(corls(X,Y,nrand = n_rand),"p.value")[1,2]
h0_alpha[k,i,2] <- vegan::protest(X,Y,permutations = n_rand)$signif
......@@ -865,11 +865,11 @@ if (compute) {
for (r in seq_along(r2s)) {
X <- simulate_matrix(n_indivduals[i],
p_qs[j],
equal.var = TRUE)
equal_var = TRUE)
Y <- simulate_correlation(X,
p_qs[j],
r2 = r2s[r],
equal.var = TRUE)
equal_var = TRUE)
h1_sims[k,i,j,r,1] <- attr(corls(X,Y,nrand = n_rand),"p.value")[1,2]
h1_sims[k,i,j,r,2] <- suppressWarnings(vegan::protest(X,Y,permutations = n_rand)$signif)
......
No preview for this file type
......@@ -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 15:28:19 2019
% Tue Oct 1 18:48:35 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 15:28:22 2019
% Tue Oct 1 18:48:38 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 15:28:22 2019
% Tue Oct 1 18:48:38 2019
\begin{tabular}{lcrrrrrrrrr}
\hline
& $R^2$ & \multicolumn{4}{c}{5\%} & &\multicolumn{4}{c}{10\%} \\
......
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