Assessment of Individual Identity in Animal Signals

Provides tools for assessment and quantification of individual identity information in animal signals. This package accompanies a research article by Linhart et al. (2019) : "Measuring individual identity information in animal signals: Overview and performance of available identity metrics".


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')

News


title: "NEWS" author: "Pavel Linhart" date: "17 April 2019" output: md_document

IDmeasurer 1.0.0

This is a first release of IDmeasurer package.

New adds

  • six example datasets
  • functions to calculate basic univariate identity metrics
  • functions to calculate basic multivariate identity metrics
  • functions to convert HS to DS and vice versa using loess prediction
  • functions to simulate univariate and multivariate identity datasets

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("IDmeasurer")

1.0.0 by Pavel Linhart, 7 months ago


Browse source code at https://github.com/cran/IDmeasurer


Authors: Pavel Linhart [aut, cre]


Documentation:   PDF Manual  


CC0 license


Imports MASS, infotheo, lme4

Suggests knitr, rmarkdown


See at CRAN