sticsy: Statistical total correlation spectroscopy with new...

In danielcanueto/sticsy: Statistical total correlation spectroscopy with new dependence measures (distance correlation, distance covariance, MIC, gMIC, TIC)

Description

Statistical total correlation spectroscopy with new dependence measures (distance correlation, distance covariance, MIC, gMIC, TIC)

Usage

sticsy(dataset, ppm, ppm_edges, method = "TIC",
  visualization_method = "median")

Arguments

dataset	Matrix of spectra x bins.
ppm	ppm.
ppm_edges	Left and right edges (in ppm) of the spectrum region to analyze.
method	Dependence measure to use for correlation spectroscopy. Available options are 'pearson', 'spearman', 'dcor' (distance correlation), 'dcov' (distance covariance), 'MIC' (Maximal Information Coefficient), 'GMIC' (Generalized Maximal Information Coefficient) and 'TIC' (Total Information Coefficient). By default TIC.
visualization_method	How to visualize results in spectra dataset analyzed. 'median' shows the median spectrum, a numeric figure shows a random n number of spectra (it is recommended not to plot more than 10). By default 'median'.

Value

Vector with results of correlation spectroscopy and Plotly interactive figure for visualization of results.

Examples

data=MTBLS1()
results=sticsy(data$dataset,data$ppm,c(7.58,7.54),method='TIC',visualization_method='median')

danielcanueto/sticsy documentation built on May 30, 2019, 8:25 a.m.