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minerva

R package for Maximal Information-Based Nonparametric Explorationcomputation

• MinepyHomepage
• MinepyGithub
• MictoolsGithub

Install

• Latest cran release

install.packages("minerva")

• Development version

devtools::install_github('filosi/minerva')

Usage

• Basic usage with helper functionmine.

library(minerva)x<-0:200/200y<-sin(10* pi* x)+ xmine(x,y,n.cores=1)

• Compute a single measure from the MINE suite usingmine_stat.
  • Available mesures are:mic,mas,mev,mcn,tic,gmic

x<-0:200/200y<-sin(10* pi* x)+ xmine_stat(x, y,measure="mic")

• To compute themic-r2 measure use thecorR function:

x<-0:200/200y<-sin(10* pi* x)+ xr2<-cor(x, y)mm<-mine_stat(x, y,measure="mic")mm- r2**2## mine(x, y, n.cores=1)[[5]]

Compute statistic onmatrices

• All features in a single matrix(mine_compute_pstat).
• All possible combination of features between two matrices(mine_compute_cstat).
  • When comparing two matrices the function check for euquality ofnumber of rows of the two matrices. If the matrices have differentnumber of rows then an error is thrown.

x<-matrix(rnorm(1000),ncol=10,nrow=10)y<-as.matrix(rnorm(1000),ncol=10,nrow=20)## Compare feature of the same matrixpstats(x)## Compare features of matrix x with feature in matrix ycstats(x, y)

Mictools pipeline

This is inspired to the original implementation by Albanese etal. available in python here:https://github.com/minepy/mictools.

Reading the data frommictool repository

datasaurus<-read.table("https://raw.githubusercontent.com/minepy/mictools/master/examples/datasaurus.txt",header=TRUE,row.names=1,as.is=TRUE,stringsAsFactors=FALSE)datasaurus.m<-t(datasaurus)

Compute null distributionfortic_e

Automatically compute:

• tic_e null distribution based on permutations.
• histogram of the distribution with cumulative distribution.
• Observed values oftic_e for each pair of variable indatasaurus.
• Observed distribution oftic_e.
• P-value for each variable pair association.

ticnull<-mictools(datasaurus.m,nperm=10000,seed=1234)## Get the names of the named listnames(ticnull)##[1]  "tic"      "nulldist" "obstic"   "obsdist"  "pval"

Null Distribution

ticnull$nulldist

Observed distribution

ticnull$obsdist

Plottic_e and pvalue distribution.

hist(ticnull$tic)hist(ticenull$pval,breaks=50,freq=FALSE)

Usep.adjust.method to use a different pvalue correctionmethod, or use theqvalue package to use Storey’sqvalue.

## Correct pvalues using qvalueqobj<-qvalue(ticnull$pval$pval)## Add column in the pval data.frameticnull$pval$qvalue<- qobj$qvalueticnull$pval

Same table as above with the qvalue column added at the end.

Strenght of the association(MIC)

## Use columns of indexes and FDR adjusted pvaluemicres<-mic_strength(datasaurus.m, ticnull$pval,pval.col=c(6,2,3))

Association strength computed based on theqvalueadjusted pvalue

## Use qvalue adjusted pvaluemicresq<-mic_strength(datasaurus.m, ticnull$pval,pval.col=c("qvalue","Var1","Var2"))

Citing minepy/minerva andmictools