| calcHSngroups {IDmeasurer} | R Documentation |
Calculate Beecher's information statistic (HS, variant = HSngroups)
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
calcHSngroups(df, sumHS = T)
Arguments
df |
A data frame with the first column indicating individual identity. |
sumHS |
|
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
Other individual identity metrics: calcDS,
calcF, calcHM,
calcHSnpergroup, calcHSntot,
calcHSvarcomp, calcHS,
calcMI, calcPICbetweenmeans,
calcPICbetweentot, calcPIC
Examples
calcHSngroups(ANmodulation)
temp <- calcPCA(ANmodulation)
calcHSngroups(temp)