plotRelevance {asmbPLS}

R Documentation

Relevance plot for asmbPLS-DA

Description

Function to visualize the most relevant features (relevant to the outcome) in each block.

Usage

plotRelevance(fit.results, n.top = 10, ncomp = 1, block.name = NULL)

Arguments

fit.results	The output of asmbPLSDA.fit or asmbPLS.fit.
n.top	A integer indicating the number of the most relevant features to be displayed for each block. The default is 10. If the number of selected features in the block is smaller than n.top, all the selected features in that block will be displayed.
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 or asmbPLS.fit. The default is 1.
block.name	A vector containing the named character for each block. It must be ordered and match each block.

Details

The function returns a plot to show the most relevant features for each block.

Value

none

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 to show the most relevant features in each block
plotRelevance(asmbPLSDA.fit.binary)
plotRelevance(asmbPLSDA.fit.multiclass)
## custom n.top and block.name
plotRelevance(asmbPLSDA.fit.binary, 
              n.top = 5,
              block.name = c("mRNA", "protein"))
plotRelevance(asmbPLSDA.fit.multiclass, 
              n.top = 7,
              block.name = c("miRNA", "protein"))

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