opticut-package {opticut}

Likelihood Based Optimal Partitioning and Indicator Species Analysis

Description

Likelihood based optimal partitioning and indicator species analysis. Finding the best binary partition for each species based on model selection, with the possibility to take into account modifying/confounding variables as described in Kemencei et al. (2014) <doi:10.1556/ComEc.15.2014.2.6>. The package implements binary and multi-level response models, various measures of uncertainty, Lorenz-curve based thresholding, with native support for parallel computations.

Details

The DESCRIPTION file:

Package:	opticut
Type:	Package
Title:	Likelihood Based Optimal Partitioning and Indicator Species Analysis
Version:	0.1-3
Date:	2024-05-21
Author:	Peter Solymos [cre, aut] (<https://orcid.org/0000-0001-7337-1740>), Ermias T. Azeria [ctb]
Maintainer:	Peter Solymos <[e-mail]>
Description:	Likelihood based optimal partitioning and indicator species analysis. Finding the best binary partition for each species based on model selection, with the possibility to take into account modifying/confounding variables as described in Kemencei et al. (2014) <doi:10.1556/ComEc.15.2014.2.6>. The package implements binary and multi-level response models, various measures of uncertainty, Lorenz-curve based thresholding, with native support for parallel computations.
URL:	https://github.com/psolymos/opticut
BugReports:	https://github.com/psolymos/opticut/issues
Depends:	R (>= 3.1.0), pbapply (>= 1.3-0)
Imports:	MASS, pscl, betareg, ResourceSelection (>= 0.3-2), parallel, mefa4
License:	GPL-2
LazyLoad:	yes
LazyData:	true

Index of help topics:

allComb                 Finding All Possible Binary Partitions
bestmodel               Best model, Partition, and MLE
beta2i                  Scaling for the Indicator Potential
birdrec                 Bird Species Detections
dolina                  Land Snail Data Set
lorenz                  Lorenz Curve Based Thresholds and Partitions
multicut                Multi-level Response Model
occolors                Color Palettes for the opticut Package
ocoptions               Options for the opticut Package
opticut                 Optimal Binary Response Model
opticut-package         Likelihood Based Optimal Partitioning and
                        Indicator Species Analysis
optilevels              Optimal Number of Factor Levels
rankComb                Ranking Based Binary Partitions
uncertainty             Quantifying Uncertainty for Fitted Objects
warblers                Warblers Data Set

The main user interface are the opticut and multicut functions to find the optimal binary or multi-level response models. Make sure to evaluate uncertainty. optilevels finds the optimal number of factor levels.

Author(s)

Peter Solymos [cre, aut] (<https://orcid.org/0000-0001-7337-1740>), Ermias T. Azeria [ctb]

Maintainer: Peter Solymos <[e-mail]>

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. <doi:10.1556/ComEc.15.2014.2.6>

Examples

## 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(formula = y ~ 1, strata = g, dist = "poisson")
summary(oc)

## visualize the results
plot(oc, cut = -Inf)

## quantify uncertainty
uc <- uncertainty(oc, type = "asymp", B = 999)
summary(uc)

## go beyond binary partitions

mc <- multicut(formula = y ~ 1, strata = g, dist = "poisson")
summary(mc)

ol <- optilevels(y[,"Sp2"], as.factor(g))
ol[c("delta", "coef", "rank", "levels")]