Description Usage Arguments Details Value Author(s) See Also Examples

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

1 2 | ```
DaMiR.corrplot(sv, df, type = c("pearson", "spearman"),
sig.level = 1e-04)
``` |

`sv` |
The matrix of sv identified by |

`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 |

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.

A correlation plot between sv and known variables.

Mattia Chiesa, Luca Piacentini

1 2 3 4 5 |

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