mutualInfo: Mutual information
In diggit: Inference of Genetic Variants Driving Cellular Phenotypes

Description Usage Arguments Details Value Examples

This function estimates the mutual information between x and y given both are numeric vectors, between the columns of x if it is a numeric matrix, or between the columns of x and y if both are numeric matrixes

1 2	mutualInfo(x, y = NULL, per = 0, pairwise = FALSE, bw = 100, cores = 1, verbose = TRUE)

`x`	Numeric vector or matrix
`y`	Optional numeric vector or matrix
`per`	Integer indicating the number of permutations to compute p-values
`pairwise`	Logical, wether columns of x and y should be compared in a pairwise maner. x and y must have the same number of columns
`bw`	Integer indicating the grid size for integrating the joint probability density
`cores`	Integer indicating the number of cores to use (1 for Windows-based systems)
`verbose`	Logical, whether progression bars should be shown

This function estimates the mutual information between continuous variables using a fix bandwidth implementation

Numeric value, vector or matrix of results

x <- seq(0, pi, length=100)
y <- 5*sin(x)+rnorm(100)
cor.test(x, y)
mutualInfo(x, y, per=100)

Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colMeans, colSums, colnames, do.call,
    duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
    lapply, lengths, mapply, match, mget, order, paste, pmax, pmax.int,
    pmin, pmin.int, rank, rbind, rowMeans, rowSums, rownames, sapply,
    setdiff, sort, table, tapply, union, unique, unsplit, which,
    which.max, which.min

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	Pearson's product-moment correlation

data:  x and y
t = 0.68527, df = 98, p-value = 0.4948
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.1291116  0.2619232
sample estimates:
       cor 
0.06905781 

Estimating kernel bandwidth...
Computing MI null model...

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  |======================================================================| 100%$mi
[1] 0.02391956

$z
[1] 6.737748

$p.value
[1] 8.043001e-12