IDmeasurer

The goal of IDmeasurer package is to provide tools for assessment and quantification of individual identity information in animal signals. This package accompanies a research article by Linhart et al.: ‘Measuring individual identity information in animal signals: Overview and performance of available identity metrics’, which can currently be accessed at BioRxive.

Installation

The package is currently available at GitHub:

devtools::install_github('pygmy83/IDmeasurer', build = TRUE, build_opts = c("--no-resave-data", "--no-manual"))

The package has been also submitted to CRAN and it should be soon possible to install the released version of IDmeasurer from CRAN with:

install.packages("IDmeasurer")

Example

This is a basic example which shows how to calculate individual identity information in territorial calls of little owls (ANspec example data):

library(IDmeasurer)

Input data for the calculation of identity metrics in this package, in general, is a data frame with the first column containing individual identity codes (factor) and the other columns containing individuality traits (numeric).

summary(ANspec)   
#>        id           dur               df              minf       
#>  007a   : 10   Min.   :0.3680   Min.   : 547.2   Min.   : 476.6  
#>  042a   : 10   1st Qu.:0.5040   1st Qu.: 955.7   1st Qu.: 742.2  
#>  045a   : 10   Median :0.5680   Median :1014.0   Median : 820.3  
#>  055a   : 10   Mean   :0.5733   Mean   :1033.0   Mean   : 798.7  
#>  062a   : 10   3rd Qu.:0.6320   3rd Qu.:1073.6   3rd Qu.: 890.6  
#>  070p   : 10   Max.   :0.9760   Max.   :1781.4   Max.   :1101.6  
#>  (Other):270                                                     
#>       maxf             q25              q50              q75        
#>  Min.   : 929.7   Min.   : 570.3   Min.   : 875.0   Min.   : 898.4  
#>  1st Qu.:1234.4   1st Qu.: 906.3   1st Qu.: 992.2   1st Qu.:1109.4  
#>  Median :1839.8   Median : 953.1   Median :1039.1   Median :1203.1  
#>  Mean   :1609.0   Mean   : 959.0   Mean   :1049.6   Mean   :1291.4  
#>  3rd Qu.:1882.8   3rd Qu.:1007.8   3rd Qu.:1084.0   3rd Qu.:1523.4  
#>  Max.   :1937.5   Max.   :1203.1   Max.   :1398.4   Max.   :1750.0  
#> 

This calculates HS metric for every single trait variable in the dataset.

calcHS(ANspec, sumHS=F)
#>   vars Pr   HS
#> 2  dur  0 1.13
#> 3   df  0 0.58
#> 4 minf  0 0.80
#> 5 maxf  0 1.06
#> 6  q25  0 1.04
#> 7  q50  0 1.48
#> 8  q75  0 0.93

To calculate the HS for an entire signal, it is neccessary to have uncorrelated variables in dataset. Raw (correlated) trait variables need to be transformed into principal components by the Principal component analysis.

temp <- calcPCA(ANspec) 

Calculate HS for an entire signal.

calcHS(temp) 
#> HS for significant vars         HS for all vars 
#>                    4.68                    4.68

To see description of the example dataset, use:

?ANspec

More examples can be found in IDmeasurer vignette:

vignette('idmeasurer-workflow-examples')