https://github.com/psolymos/opticut
Likelihood Based Optimal Partitioning
https://github.com/psolymos/opticut
cran ecology indicator-species-analysis likelihood optimal-partitioning r species
Last synced: about 1 year ago
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Likelihood Based Optimal Partitioning
- Host: GitHub
- URL: https://github.com/psolymos/opticut
- Owner: psolymos
- Created: 2015-06-15T17:20:36.000Z (about 11 years ago)
- Default Branch: master
- Last Pushed: 2024-05-21T16:50:24.000Z (about 2 years ago)
- Last Synced: 2025-04-17T05:54:44.211Z (over 1 year ago)
- Topics: cran, ecology, indicator-species-analysis, likelihood, optimal-partitioning, r, species
- Language: R
- Homepage: https://github.com/psolymos/opticut
- Size: 8.86 MB
- Stars: 2
- Watchers: 1
- Forks: 1
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# opticut: Likelihood Based Optimal Partitioning and Indicator Species Analysis
[](https://CRAN.R-project.org/package=opticut)
[](https://CRAN.R-project.org/package=opticut)
[](https://www.gnu.org/licenses/old-licenses/gpl-2.0.en.html)
Likelihood based optimal partitioning for indicator species analysis and more.
Finding the best binary partition for each species based on
model selection, possibly controlling for modifying/confounding
variables as described in Kemencei et al. (2014).
## Versions
Install stable version from CRAN:
```R
install.packages("opticut")
```
Install development version from GitHub:
```R
devtools::install_github("psolymos/opticut")
```
User visible changes in the package are listed in the [NEWS](https://github.com/psolymos/opticut/blob/master/NEWS.md) file.
## Report a problem
Use the [issue tracker](https://github.com/psolymos/opticut/issues)
to report a problem.
## Typical workflow
```R
library(opticut)
## --- community data ---
y <- cbind(
Sp1 = c(4,6,3,5, 5,6,3,4, 4,1,3,2),
Sp2 = c(0,0,0,0, 1,0,0,1, 4,2,3,4),
Sp3 = c(0,0,3,0, 2,3,0,5, 5,6,3,4))
## --- stratification ---
g <- c(1,1,1,1, 2,2,2,2, 3,3,3,3)
## --- find optimal partitions for each species ---
oc <- opticut(y, strata = g, dist = "poisson")
summary(oc)
# Multivariate opticut results, comb = rank, dist = poisson
#
# Call:
# opticut.default(Y = y, strata = g, dist = "poisson")
#
# Best supported models with logLR >= 2:
# split assoc I mu0 mu1 logLR w
# Sp3 2 3 ++ 0.6471 0.75 3.50 4.793 0.6962
# Sp2 3 +++ 0.8571 0.25 3.25 9.203 0.9577
# 2 binary splits
# 1 species not shown
## --- visualize the results ---
plot(oc, cut = -Inf)
## --- quantify uncertainty ---
uc <- uncertainty(oc, type = "asymp", B = 999)
summary(uc)
# Multivariate opticut uncertainty results
# type = asymp, B = 999, level = 0.95
#
# split R I Lower Upper
# Sp1 1 2 1 0.2860 0.02341 0.5668
# Sp3 2 3 1 0.6218 0.21456 0.8813
# Sp2 3 1 0.8274 0.51229 0.9680
```
## Dynamic documents with opticut
Here is a minimal [Rmarkdown](https://rmarkdown.rstudio.com/) example: [Rmd](https://raw.githubusercontent.com/psolymos/opticut/master/extras/opticut-knitr-example.Rmd) source, knitted [PDF](https://github.com/psolymos/opticut/raw/master/extras/opticut-knitr-example.pdf).
## References
Kemencei, Z., Farkas, R., Pall-Gergely, B., Vilisics, F., Nagy, A., Hornung,
E. & Solymos, P. (2014): Microhabitat associations of land snails in
forested dolinas: implications for coarse filter conservation.
_Community Ecology_ **15**:180--186.
[[link](https://dx.doi.org/10.1556/ComEc.15.2014.2.6), [PDF](https://drive.google.com/file/d/0B-q59n6LIwYPWnBjLUxvcXJVUXc/view?usp=sharing)]