pg {divo} | R Documentation |
pg Power-Geometric Index
Description
The Power Geometric (PG) index is a geometric angular overlap measure parameterized by a two-dimensional vector (alpha, beta). The PG index is a generalization of the Morisita-Horn index as well as the Bhattacharyya's coefficient. It allows for increasing or decreasing the relative contribution of the rare species to the overall overlap and may be therefore used to account for the species undersampling. It quantifies overlap as cosine of an angle between two exponentially normalized population vectors. For further details and definition, see Rempala and Seweryn (2013).
Usage
pg(x, alpha = 1, beta=alpha, CI = 0.95, resample = 100, graph = FALSE,
csv_output = FALSE, PlugIn = FALSE, size = 1, CVG = FALSE,
saveBootstrap = FALSE)
Arguments
x |
a matrix containing input populations |
alpha |
PG of order alpha < 1 puts more weight on the rare species and the I Index of order alpha > 1 puts more weight on the abundant ones for first population, default = 1 |
beta |
PG of order beta < 1 puts more weight on the rare species and the I Index of order beta > 1 puts more weight on the abundant ones for second population, default = alpha |
CVG |
PG of order alpha or beta = coverage. If CVG = TRUE argument alpha is ignored; default = FALSE |
CI |
Confidence Interval default = 0.95, range (0, 1) |
resample |
number of repetitions, default = 100 |
graph |
default = FALSE, plot the results of hierarchical clustering of pairwise analysis of Power-Geometric Index; |
csv_output |
save the result of the analysis as .CSV file, default = FALSE; |
PlugIn |
standard plug-in estimator, default = FALSE |
size |
resampled fraction of the population, default = 1 (actual size of populations). The value should not be smaller than 10% of population (size = 0.1) |
saveBootstrap |
Saves bootstrap result to a file. Use saveBootstrap = TRUE to save bootstrap results to a Bootstrap folder in current directory; saveBootstrap = 'FolderName' - saves bootstrap results to user-named folder |
Author(s)
Christoph Sadee, Maciej Pietrzak, Michal Seweryn, Cankun Wang, Grzegorz Rempala
Maintainer: Maciej Pietrzak pietrzak.20@osu.edu
References
Rempala G.A., Seweryn M. (2013) Methods for diversity and overlap analysis in T-cell receptor populations. J Math Biol 67:1339-68
Examples
data(TCR.Data)
result <- pg(x, resample = 20)