R/pca.r

In MultiGroupO: MultiGroup Method and Simulation Data Analysis

#' Performs a principal components analysis in the
#' given data matrix.
#' Show PCA graphical output.
#'
#' @importFrom stats prcomp
#' @importFrom ggplot2 ggplot geom_point scale_color_brewer theme geom_density scale_fill_brewer aes xlab ylab
#' @importFrom gridExtra grid.arrange
#' @importFrom lemon grid_arrange_shared_legend
#' @param datos is a matrix with the data
#' @param grupos is a vector of classes
#' @param Plot vector logic for grafic
#' @param center data set center by columns
#' @param scale  data set scaled by columns
#'
#' @return \strong{return an grafics}.
#'
#' @examples
#' library(plsgenomics)
#' data(SRBCT)
#' mydata<-SRBCT$X
#' mydata<-mydata[1:30,1:20]
#' groups<-as.factor(SRBCT$Y)[1:30]
#' pca(datos=mydata,grupos=groups,Plot=TRUE,center=TRUE,scale=TRUE)
#' @export
pca<-function(datos,grupos,Plot=TRUE,center=TRUE,scale=TRUE)
{
  PC1<-NULL
  PC2<-NULL
  PC3<-NULL
  pcs<-prcomp(datos,center =center, scale =scale)
  pcdatos<-as.data.frame(pcs$x)
  pcdatos<-cbind(pcdatos,grupos)
  group<-pcdatos$grupos

  percentage1 <- round((pcs$sdev^{2}/sum(pcs$sdev^{2}))*100,2)
  percentage <- paste(colnames(pcdatos), "(", paste( as.character(percentage1),
                                                     "%", ")", sep="") )

  if(Plot)
  {
    scatterPlot1 <- ggplot(pcdatos,aes(PC1, PC2, color=group))+
      geom_point(size = 3) + xlab(percentage[1]) + ylab(percentage[2])+
      scale_color_brewer(palette="Set2")


    scatterPlot2 <- ggplot(pcdatos,aes(PC1, PC3, color=group)) +
      geom_point(size = 3) + xlab(percentage[1]) + ylab(percentage[3])+
      scale_color_brewer(palette="Set2")


    scatterPlot3 <- ggplot(pcdatos,aes(PC2, PC3, color=group)) +
      geom_point(size = 3) + xlab(percentage[2]) + ylab(percentage[3])+
      scale_color_brewer(palette="Set2")


    xdensity <- ggplot(pcdatos, aes(PC1, fill=group)) +
      geom_density(alpha=.5) +
      scale_fill_brewer(palette="Set2")


    ydensity <- ggplot(pcdatos, aes(PC2, fill=group)) +
      geom_density(alpha=.5) +
      scale_fill_brewer(palette="Set2")


    zdensity <- ggplot(pcdatos, aes(PC3, fill=group)) +
      geom_density(alpha=.5) +
      scale_fill_brewer(palette="Set2")


  nt <- theme(legend.position='hidden')
  grid_arrange_shared_legend(xdensity+nt,  zdensity+nt,ydensity+nt,scatterPlot1, scatterPlot2+nt, scatterPlot3+nt, nrow=2,ncol=3)
  }

  # calculate explained variance
  prop_expl_var <- pcs$sdev^{2} / sum(pcs$sdev^{2})

  r<-list(loadings=pcs$rotation,variates=pcs$x,prop_expl_var=prop_expl_var)
  r
  class(r) = c("pca")
  return(r)
}