DaMiR.corrplot {DaMiRseq} | R Documentation

## Correlation Plot

### Description

This function easily draws the correlation plot of surrogate variables (sv) and variables.

### Usage

    DaMiR.corrplot(sv, df, type = c("pearson", "spearman"),
      sig.level = 1e-04)

### Arguments

`sv` | The matrix of sv identified by `DaMiR.SV` function
`df` | A data frame with class and known variables; at least one column with 'class' label must be included
`type` | Type of correlation metric to be applied; default is "pearson"
`sig.level` | The significance level of the correlation; default is 0.0001

### Details

Factorial variables are allowed. They will be tranformed as numeric before applying the `rcorr` function of `Hmisc`.The `corrplot` function, which draws the plot, marks with a cross all the correlations that do not reach the significance threshold defined in the `sig.level` argument.This plot allows the user to identify those sv that present significant correlations with either technical and biological known variables. Notably, none of the sv should present signifcant correlation with "class" variable.

### Value

A correlation plot between sv and known variables.

### Author(s)

Mattia Chiesa, Luca Piacentini

### See Also

`DaMiR.SV`

### Examples

    # use example data:
    data(df)
    data(sv)
    # Draw correlation plot:
    #DaMiR.corrplot(sv=sv, df=df, type = "pearson", sig.level=0.01)

[Package DaMiRseq version 2.4.3 Index]