asmbPLS (version 1.0.0)

plotCor: Graphical output for the asmbPLS-DA framework

Description

Function to visualize correlations between PLS components from different blocks using the model fitted by the function asmbPLSDA.fit.

Usage

plotCor(
  fit.results,
  ncomp = 1,
  block.name = NULL,
  group.name = NULL,
  legend = TRUE
)

Value

none

Arguments

fit.results

The output of asmbPLSDA.fit.

ncomp

Which component to plot from each block. Should not be larger than the number of PLS components used (PLS.comp) in the function asmbPLSDA.fit. The default is 1.

block.name

A vector containing the named character for each block. It must be ordered and match each block.

group.name

A vector containing the named character for each sample group. For binary outcome, first group name matches Y.matrix = 0, second group name matches Y.matrix = 1. For multiclass outcome, ith group name matches ith column of Y.matrix = 1.

legend

A logical value indicating whether the legend should be added. The default is TRUE.

Details

The function returns a plot to show correlations between PLS components from different blocks. The lower triangular panel indicates Pearson's correlation coefficient, and the upper triangular panel the scatter plot.

Examples

## Use the example dataset
data(asmbPLSDA.example)
X.matrix = asmbPLSDA.example$X.matrix
Y.matrix.binary = asmbPLSDA.example$Y.matrix.binary
Y.matrix.multiclass = asmbPLSDA.example$Y.matrix.morethan2levels
X.dim = asmbPLSDA.example$X.dim
PLS.comp = asmbPLSDA.example$PLS.comp
quantile.comb = asmbPLSDA.example$quantile.comb
 
## asmbPLSDA fit for binary outcome
asmbPLSDA.fit.binary <- asmbPLSDA.fit(X.matrix = X.matrix, 
                                      Y.matrix = Y.matrix.binary, 
                                      PLS.comp = PLS.comp, 
                                      X.dim = X.dim, 
                                      quantile.comb = quantile.comb,
                                      outcome.type = "binary")

## asmbPLSDA fit for categorical outcome with more than 2 levels
asmbPLSDA.fit.multiclass <- asmbPLSDA.fit(X.matrix = X.matrix, 
                                          Y.matrix = Y.matrix.multiclass,
                                          PLS.comp = PLS.comp, 
                                          X.dim = X.dim, 
                                          quantile.comb = quantile.comb,
                                          outcome.type = "multiclass")

## visualization with default block.name and group.name using the first PLS component 
plotCor(asmbPLSDA.fit.binary, 1)
plotCor(asmbPLSDA.fit.multiclass, 1)
## custom block.name and group.name
plotCor(asmbPLSDA.fit.binary, 
        ncomp = 1, 
        block.name = c("mRNA", "protein"), 
        group.name = c("control", "case"))
plotCor(asmbPLSDA.fit.multiclass, 
        ncomp = 1, 
        block.name = c("mRNA", "protein"), 
        group.name = c("healthy", "mild", "severe"))