pruneindicators.RdThis function allows reducing drastically the number of species combinations to be retained for a given target site group.
pruneindicators(
x,
At = 0,
Bt = 0,
sqrtIVt = 0,
alpha = 1,
max.indicators = 4,
verbose = FALSE
)An object of class 'indicators'.
Threshold for positive predictive value. Combinations with lower values are not kept.
Threshold for sensitivity. Combinations with lower values are not kept.
Threshold for (square root of) indicator value. Combinations with lower values are not kept.
Threshold for statistical significance of indicator value. Combinations with higher p-values are not kept.
Maximum number of species combinations to be kept. If NULL, the function returns all the non-nested valid indicators without further selection.
If TRUE, prints the results of each step.
An object of class 'indicators' with only the species combinations selected.
First, the function selects those indicators (species or species combinations) with valid positive predictive value, sensitivity and indicator value, according to the input thresholds. If the object 'speciescomb' contains confidence intervals, then the lower bounds are used to select the valid indicators. Second, the function discards those valid indicators whose occurrence pattern is nested within other valid indicators. Third, the function evaluates the coverage of the remaining set of indicators and explores subsets of increasing number of indicators, until the same coverage is attained and the set of indicators is returned. If the maximum allowed members is attained (max.indicators) then the set of indicators with maximum coverage is returned.
De Cáceres, M., Legendre, P., Wiser, S.K. and Brotons, L. 2012. Using species combinations in indicator analyses. Methods in Ecology and Evolution 3(6): 973-982.
De Cáceres, M. and Legendre, P. 2009. Associations between species and groups of sites: indices and statistical inference. Ecology 90(12): 3566-3574.
library(stats)
data(wetland) ## Loads species data
## Creates three clusters using kmeans
wetkm <- kmeans(wetland, centers=3)
## Run indicator analysis with species combinations for the first group
sc <- indicators(X=wetland, cluster=wetkm$cluster, group=1, verbose=TRUE, At=0.5, Bt=0.2)
#> Target site group: 1
#> Number of candidate species: 33
#> Number of sites: 41
#> Size of the site group: 10
#> Starting species 1 ... accepted combinations: 0
#> Starting species 2 ... accepted combinations: 16
#> Starting species 3 ... accepted combinations: 16
#> Starting species 4 ... accepted combinations: 87
#> Starting species 5 ... accepted combinations: 87
#> Starting species 6 ... accepted combinations: 133
#> Starting species 7 ... accepted combinations: 133
#> Starting species 8 ... accepted combinations: 153
#> Starting species 9 ... accepted combinations: 155
#> Starting species 10 ... accepted combinations: 155
#> Starting species 11 ... accepted combinations: 155
#> Starting species 12 ... accepted combinations: 155
#> Starting species 13 ... accepted combinations: 155
#> Starting species 14 ... accepted combinations: 155
#> Starting species 15 ... accepted combinations: 155
#> Starting species 16 ... accepted combinations: 160
#> Starting species 17 ... accepted combinations: 161
#> Starting species 18 ... accepted combinations: 170
#> Starting species 19 ... accepted combinations: 170
#> Starting species 20 ... accepted combinations: 172
#> Starting species 21 ... accepted combinations: 172
#> Starting species 22 ... accepted combinations: 173
#> Starting species 23 ... accepted combinations: 173
#> Starting species 24 ... accepted combinations: 173
#> Starting species 25 ... accepted combinations: 173
#> Starting species 26 ... accepted combinations: 173
#> Starting species 27 ... accepted combinations: 173
#> Starting species 28 ... accepted combinations: 173
#> Starting species 29 ... accepted combinations: 173
#> Starting species 30 ... accepted combinations: 173
#> Starting species 31 ... accepted combinations: 173
#> Starting species 32 ... accepted combinations: 173
#> Starting species 33 ... accepted combinations: 173
#> Number of valid combinations: 173
#> Number of remaining species: 13
#> Calculating statistical significance (permutational test)...
## Finds the 'best' subset of indicators
sc2 <- pruneindicators(sc, At=0.5, Bt=0.2, verbose=TRUE)
#> Coverage of initial set of 173 indicators: 100%
#> Coverage of valid set of 173 indicators: 100%
#> Coverage of valid set of 7 nonnested indicators: 100%
#> Checking 7 subsets of 1 indicator(s) maximum coverage: 90%
#> Checking 21 subsets of 2 indicator(s).......... maximum coverage: 100%
#> Coverage of final set of 2 indicators: 100%
print(sc2)
#> A B sqrtIV p.value
#> Pancam 0.6923077 0.8 0.7442084 0.005
#> Melcor+Sessp. 0.5714286 0.7 0.6324555 0.005