## File: pstats.Rd

package info (click to toggle)
r-cran-minerva 1.5.8-2
 1234567891011121314151617181920212223242526272829303132333435363738394041424344454647 % Generated by roxygen2: do not edit by hand % Please edit documentation in R/RcppExports.R \name{pstats} \alias{pstats} \title{Compute pairwise statistics (MIC and normalized TIC) between variables (convenience function).} \usage{ pstats(x, alpha = 0.6, C = 15, est = "mic_approx") } \arguments{ \item{x}{Numeric Matrix of m-by-n with n variables and m samples.} \item{alpha}{number (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).} \item{C}{number (> 0) determines how many more clumps there will be than columns in every partition. Default value is 15, meaning that when trying to draw x grid lines on the x-axis, the algorithm will start with at most 15*x clumps.} \item{est}{string ("mic_approx", "mic_e") estimator. With est="mic_approx" the original MINE statistics will be computed, with est="mic_e" the equicharacteristic matrix is is evaluated and MIC_e and TIC_e are returned.} } \value{ A matrix of (n x (n-1)/2) rows and 4 columns. The first and second column are the indexes relative to the columns in the input matrix \code{x} for which the statistic is computed for. Column 3 contains the MIC statistic, while column 4 contains the normalized TIC statistic. } \description{ For each statistic, the upper triangle of the matrix is stored by row (condensed matrix). If m is the number of variables, then for i < j < m, the statistic between (col) i and j is stored in k = m*i - i*(i+1)/2 - i - 1 + j. The length of the vectors is n = m*(m-1)/2. } \examples{ ## Create a matrix of random numbers ## 10 variables x 100 samples x <- matrix(rnorm(1000), ncol=10) res <- pstats(x) head(res) }