R/PlotBasisVectors.R

In balsorjl/PlasticityPhenotypes: Creates a plasticity phenotype using features that correlate with PCA basis vectors

#' Plot amplitudes of each variable about the basis vectors
#'
#' Plots the amplitude of the variables about each PCA basis vector identified using the cum_var function
#' @param cum.var The product identified using the cum_var function
#' @param pca.var.coord Consults the PCA object created using the FactoMineR package for the desired PCA basis vectors
#' @return A series of plots depicting the amplitude of each variable about each PCA basis vector. Each PCA basis vectors is plotted separately, and each variable is represented along the x-axis. The user should use these plots to identify variables that might interact to create new features (e.g. such as indices  or sums of variables).

amplitude_plots <- function(cum.var,pca.var.coord){

  cum.var = cum.var

  pca.var.coord = pca.var.coord

  while (T) {

    for (i in 1:cum.var){

      name <- paste0("Amplitude (Basis Vector ",i,")")

      VarCoordDim1<-data.frame(pca.var.coord[,i])

      setDT(VarCoordDim1, keep.rownames = TRUE)[]

      xyz <- ggplot(data=VarCoordDim1,
                    aes(rn,pca.var.coord...i.))+
        geom_col(colour="black")+
        scale_y_continuous(expand =c(0,0),name=name)+
        scale_x_discrete(limits=VarCoordDim1$rn)+
        theme(axis.line.y=element_line(),
              axis.line.x=element_line(),
              panel.grid=element_blank(),
              axis.text.x = element_text(angle=45,hjust =1,size=12),
              axis.text.y = element_text(angle=0,vjust=0.5,size=12),
              axis.title.x = element_text(size=14,face="bold"),
              axis.title.y = element_text(size=14,face="bold"),
              panel.grid.major = element_blank(),
              panel.grid.minor = element_blank())+
        xlab("Proteins")

      print(xyz)

    }

    break

  }

}