File: mictools.Rd

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r-cran-minerva 1.5.8-2
 12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758596061626364656667686970717273747576777879 % Generated by roxygen2: do not edit by hand % Please edit documentation in R/mictools.R \name{mictools} \alias{mictools} \title{Function that implements the \code{mictools} pipeline. In particular it computes the null and observed distribution of the \code{tic_e} measure} \usage{ mictools(x, alpha = 9, C = 5, seed = 0, nperm = 2e+05, p.adjust.method = "BH") } \arguments{ \item{x}{a numeric matrix with N samples on the rows and M variables on the columns (NxM).} \item{alpha}{float (0, 1.0] or >=4 if alpha is in (0,1] then B will be max(n^alpha, 4) where n is the number of samples. If alpha is >=4 then alpha defines directly the B parameter. If alpha is higher than the number of samples (n) it will be limited to be n, so B = min(alpha, n) Default value is 0.6 (see Details).} \item{C}{a positive integer number, the \code{C} parameter of the \code{mine} statistic. See \code{\link[minerva]{mine}} function for further details.} \item{seed}{seed for random number generation reproducibility} \item{nperm}{integer, number of permutation to perform} \item{p.adjust.method}{method for pvalue adjustment, see \code{\link[stats]{p.adjust}} for available methods.} } \value{ A list of 5 named elements containing the following information of the computed statistic: \describe{ \item{tic}{This is a vector with the null distribution of tic_e values based on the permutation.} \item{nulldist}{Null distribution of the \code{tic_e} measure. It is a \code{data.frame} of 4 columns containing the histogram of the distribution of \code{tic_e} for each bin delimited by \code{BinStart} and \code{BinEnd}, the count for each bin \code{NullCount} and the cumulative distribution of the right tail area \code{NullCumSum}} \item{obstic}{\code{data.frame} with the observed \code{tic_e} values, the indexes of the variables between the tic is computed. If the input matrix has column names then the names are reported in the dataframe, otherwise "Var" is added for each variable.} \item{obsdists}{\code{data.frame} similar to \code{nulldist} but with observed values of \code{tic_e}} \item{pval}{data.frame with the pvalue computed for each comparison. The adjusted pvalue is also reported based on the method chosen with the parameter \code{p.adjust.method}} } } \description{ Function that implements the \code{mictools} pipeline. In particular it computes the null and observed distribution of the \code{tic_e} measure } \details{ This is a function to implement the mictools pipeline. Differently from the python pipeline available on github we consider a data matrix of NxM with N samples by rows and M variables by columns as standard for R. } \examples{ data(Spellman) Spellman <- as.matrix(Spellman) spellress <- mictools(Spellman[, 10:20], nperm=1000) ## Use a different pvalue correction method spellressb <- mictools(Spellman[,10:20], nperm=1000, seed=1234, p.adjust.method="bonferroni") ## Distribution of tic_e null hist(spellress$tic, breaks=100, main="Tic_e null distribution") barplot(spellress$nulldist$NullCount) ## Distribution of the observed tic hist(spellress$obstic$TIC) barplot(spellress$obsdist$Count) ## Distribution of empirical pvalues hist(spellress$pval\$pval, breaks=50) } \references{ D. Albanese, S. Riccadonna, C. Donati, P. Franceschi (2018) _A practical tool for Maximal Information Coefficient Analysis_ GigaScience, 7, 4, \url{https://doi.org/10.1093/gigascience/giy032} } \seealso{ \code{\link[stats]{p.adjust}}, \code{\link[graphics]{hist}}, \code{\link[minerva]{mine}} }