opticut-package: Likelihood Based Optimal Partitioning and Indicator Species...

In 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

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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], 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")]

Example output

Loading required package: pbapply
opticut 0.1-2 	 2018-02-01
Multivariate opticut results, comb = rank, dist = poisson

Call:
opticut.formula(formula = y ~ 1, 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

Multivariate multicut uncertainty results
type = asymp, B = 999, level = 0.95

    split R      I   Lower  Upper
Sp1   1+2 1 0.2844 0.02761 0.5628
Sp3   2+3 1 0.6179 0.21122 0.8800
Sp2     3 1 0.8293 0.52669 0.9668
Multivariate multticut results, dist = poisson

Call:
multicut.formula(formula = y ~ 1, strata = g, dist = "poisson")

Species models with logLR >= 2:
    split assoc      I  logLR
Sp2     3   +++ 1.0000 10.589
Sp3     3    ++ 0.7143  5.952
1 species not shown

$delta
[1]  0.0000000 -0.4980423         NA

$coef
        1    2    3
[1,] 0.00 0.50 3.25
[2,] 0.25 0.25 3.25
[3,]   NA   NA   NA

$rank
      1  2  3
[1,]  1  2  3
[2,]  1  1  2
[3,] NA NA NA

$levels
$levels[[1]]
  1   2   3 
"1" "2" "3" 

$levels[[2]]
    1     2     3 
"1+2" "1+2"   "3" 

opticut documentation built on May 2, 2019, 5:09 a.m.