Package: IDmeasurer 1.0.0
Pavel Linhart
IDmeasurer: 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) <doi:10.1101/546143>: "Measuring individual identity information in animal signals: Overview and performance of available identity metrics".
Authors:
IDmeasurer_1.0.0.tar.gz
IDmeasurer_1.0.0.tar.gz(r-4.5-noble)IDmeasurer_1.0.0.tar.gz(r-4.4-noble)
IDmeasurer_1.0.0.tgz(r-4.4-emscripten)IDmeasurer_1.0.0.tgz(r-4.3-emscripten)
IDmeasurer.pdf |IDmeasurer.html✨
IDmeasurer/json (API)
NEWS
| # Install 'IDmeasurer' in R: |
| install.packages('IDmeasurer', repos = 'https://cloud.r-project.org') |
- ANmodulation - Little owl, _Athene noctua_ - frequency modulation
- ANspec - Little owl, _Athene noctua_ - spectrum properties
- CCformants - Corncrake, _Crex crex_ - formants
- CCspec - Corncrake, _Crex crex_ - spectrum properties
- LAhighweewoo - Yellow-breasted boubou, _Laniarius atroflavus_ - spectrum properties
- SSgrunts - Domestic pig, _Sus scrofa domestica_ - piglet grunts
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 6 years agofrom:c89c6d520a. Checks:3 OK. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Mar 20 2025 |
| R-4.5-linux | OK | Mar 20 2025 |
| R-4.4-linux | OK | Mar 20 2025 |
Exports:calcDistTcalcDistWcalcDScalcFcalcHMcalcHScalcHSngroupscalcHSnpergroupcalcHSntotcalcHSvarcompcalcMeanVeccalcMIcalcPCAcalcPICcalcPICbetweenmeanscalcPICbetweentotconvertDStoHSconvertHStoDSGenerateMultivariateGenerateUnivariate
Dependencies:bootinfotheolatticelme4MASSMatrixminqanlmenloptrrbibutilsRcppRcppEigenRdpackreformulas
Citation
To cite IDmeasurer in publications use:
Linhart P, Osiejuk T, Budka M, Salek M, Spinka M, Policht R, Syrova M, Blumstein DT. 2019. Measuring individual identity information in animal signals: Overview and performance of available identity metrics. BioRxiv, 546143,<doi:10.1101/546143>.
Corresponding BibTeX entry:
@Article{,
title = {Measuring individual identity information in animal
signals: Overview and performance of available identity metrics},
author = {Pavel Linhart and Tomasz Osiejuk and Michal Budka and
Martin Salek and Marek Spinka and Richard Policht and Michaela
Syrova and Daniel T. Blumstein},
journal = {BioRxive},
year = {2019},
pages = {546143},
doi = {10.1101/546143},
url = {https://www.biorxiv.org/content/10.1101/546143v1},
}
Readme and manuals
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')