calcHSvarcomp: Calculate Beecher's information statistic (HS, variant =...

In IDmeasurer: Assessment of Individual Identity in Animal Signals

Description

This function calculates Beecher's information statistic (HS) for all variables within the dataset.

Reference: from Beecher, M. D. (1989). Signaling Systems for Individual Recognition - an Information-Theory Approach. Animal Behaviour, 38, 248-261. doi:10.1016/S0003-3472(89)80087-9.

calcHS (equivalent to calcHSnpergroup) is the correct variant of the function calculating Beechers information statistic. The other variants use total sample size (calcHSntot) or number of individuals in dataset (calcHSngroups) instead of number of samples per individual to calculate HS. calcHSvarcomp calculates HS from variance components of mixed models. HS values calculated by calcHSvarcomp were found to be twice as large compared to HS calculated by standard approach.

Please note, sumHS = TRUE should be used in datasets where individuality traits are uncorrelated. If traits are correlated, Principal component analysis (PCA) should be applied and HS should be calculated on uncorrelated principal componenets instead of original trait variables.

Usage

calcHSvarcomp(df, sumHS = T)

Arguments

df	A data frame with the first column indicating individual identity.
sumHS	sumHS = TRUE (default) will sum partial HS values of each trait variable; sumHS = FALSE provides partial HS values separately for each individuality trait in a dataset.

Value

For sumHS = TRUE: Numeric vector of two elements indicating indicating: 1) HS summed over variables that significantly differ between individuals (in one-way Anova with individual as independent and a specific signal trait as dependent variable; or 2) HS summed over all variables in dataset.

For sumHS = FALSE: Data frame with thre columns and number of rows equal to number of variables in dataset. First column includes names of traits considered for individuality. Second column includes significance test for each trait (from one-way ANOVA with individual identity as independent factor and trait as dependent variable). Third column includes values of HS for each variable trait.

See Also

calcPIC, calcHS

Other individual identity metrics: calcDS, calcF, calcHM, calcHSngroups, calcHSnpergroup, calcHSntot, calcHS, calcMI, calcPICbetweenmeans, calcPICbetweentot, calcPIC

Examples

calcHSvarcomp(ANmodulation)
temp <- calcPCA(ANmodulation)
calcHSvarcomp(temp)

IDmeasurer documentation built on May 9, 2019, 5:02 p.m.