opticut (version 0.1-3)

opticut-package: tools:::Rd_package_title("opticut")

Description

tools:::Rd_package_description("opticut")

Author

tools:::Rd_package_author("opticut")

Maintainer: tools:::Rd_package_maintainer("opticut")

Details

The DESCRIPTION file: tools:::Rd_package_DESCRIPTION("opticut") tools:::Rd_package_indices("opticut")

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.

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")]