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minerva

R package for Maximal Information-Based Nonparametric Exploration computation

• Minepy Homepage
• Minepy Github
• Mictools Github

Install

• Latest cran release

install.packages("minerva")

• Development version

devtools::install_github('filosi/minerva')

Usage

• Basic usage with helper function mine.

library(minerva)

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

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

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

• To compute the mic-r2 measure use the cor R function:

x <- 0:200 / 200
y <- sin(10 * pi * x) + x

r2 <- cor(x, y)
mm <- mine_stat(x, y, measure="mic")
mm - r2**2

## mine(x, y, n.cores=1)[[5]]

Compute statistic on matrices

• 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 of number of rows of the two matrices. If the matrices have different number 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 matrix
pstats(x)

## Compare features of matrix x with feature in matrix y
cstats(x, y)

Mictools pipeline

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

Reading the data from mictool 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 distribution for tic_e

Automatically compute:

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

ticnull <- mictools(datasaurus.m, nperm=10000, seed=1234)

## Get the names of the named list
names(ticnull)
##[1]  "tic"      "nulldist" "obstic"   "obsdist"  "pval"

Null Distribution

ticnull$nulldist

Observed distribution

ticnull$obsdist

Plot tic_e and pvalue distribution.

hist(ticnull$tic)

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

Use p.adjust.method to use a different pvalue correction method, or use the qvalue package to use Storey’s qvalue.

## Correct pvalues using qvalue
qobj <- qvalue(ticnull$pval$pval)

## Add column in the pval data.frame
ticnull$pval$qvalue <- qobj$qvalue
ticnull$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 pvalue 
micres <- mic_strength(datasaurus.m, ticnull$pval, pval.col=c(6, 2, 3))

Association strength computed based on the qvalue adjusted pvalue

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

Citing minepy/minerva and mictools

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.