Determines the indicator value of species combinations.
indicators(
X,
cluster,
group,
func = "IndVal",
min.order = 1,
max.order = 5,
max.indicators = NULL,
At = 0,
Bt = 0,
sqrtIVt = 0,
control = how(),
permutations = NULL,
print.perm = FALSE,
nboot.ci = NULL,
alpha.ci = 0.05,
XC = TRUE,
enableFixed = FALSE,
verbose = FALSE
)# S3 method for indicators
print(
x,
At = 0,
Bt = 0,
sqrtIVt = 0,
alpha = 1,
selection = NULL,
confint = FALSE,
...
)
# S3 method for indicators
summary(object, ...)
# S3 method for indicators
plot(x, type = "sqrtIV", maxline = TRUE, ...)
An object of class indicators with:
The vector of initial candidate species.
The vector of species finally selected for combinations.
A matrix describing all the combinations studied.
A matrix containing the abundance/occurrence of each species combination.
Positive predictive power of species combinations. If nboot is not missing then this includes the lower and upper bounds of the confidence interval.
Sensitivity of species combinations. If nboot is not missing then this includes the lower and upper bounds of the confidence interval.
Square root of indicator value of species combinations. If nboot is not missing then this includes the lower and upper bounds of the confidence interval.
P-value of the permutation test of statistical significance.
A logical vector indicating the membership to the target group.
A community data table with sites in rows and species in columns. This table can contain either presence-absence or abundance data.
A vector containing the classification of sites into site groups.
The label corresponding to the target site group.
The indicator value variant to be used, either "IndVal" (non-equalized) or "IndVal.g" (group-equalized).
Minimum number of species conforming species combinations.
Maximum number of species conforming species combinations.
Maximum number of valid indicators to be kept. If NULL, then all valid indicators are kept.
Threshold for positive predictive value used to select valid indicators. Combinations with lower values are not kept.
Threshold for sensitivity used to select valid indicators. Combinations with lower values are not kept.
Threshold for (square root of) indicator value. Combinations with lower values are not kept.
A list of control values describing properties of the permutation test design, as returned by a call to how.
A custom matrix of permutations, to be used if control = NULL, with permutations in rows and site indices in columns.
If TRUE, prints permutation numbers after each set of 100 permutations.
Number of bootstrap samples for confidence intervals. If nboot.ci = NULL then confidence intervals are not estimated.
Error in confidence intervals.
If TRUE, outputs the abundance/occurrence matrix of species combinations.
If TRUE, uses species that occur in all sites as fixed elements and creates combinations with the remaining ones.
If TRUE, prints the results of each step.
An object of class 'indicators'.
Threshold for statistical significance of indicator value. Combinations with higher p-values are not kept.
A logical vector used to restrict, a priori, the species combinations to be printed.
Flag to indicate that confidence interval bounds are desired when printing.
Additional arguments for functions print,summary or plot.
An object of class 'indicators'.
Statistic to plot. Accepted values are "IV" (indicator value), "sqrtIV" (square root of indicator value), "A", "LA", "UA", (positive predictive value and confidence limits), "B", "LB", "UB" (sensitivity and confidence limits).
Flag to indicate whether a line has to be drawn joining the maximum values for each order of combinations.
Miquel De Cáceres Ainsa, EMF-CREAF
Function indicators creates explores the indicator value of the simultaneous occurrence of sets of species (i.e. species combinations). The method is described in De Cáceres et al. (2012) and is a generalization of the Indicator Value method of Dufrêne & Legendre (1997). The minimum and maximum number of species conforming the species combination can be controlled using min.order or max.order. For each combination of species it determines its positive predictive value (A), sensitivity (B) and the square root of indicator value (sqrtIV). Statistical significance of indicators for the target site group is determined by internal calls to function signassoc. Additionally, if nboot.ci is not null then bootstrap confidence intervals are determined with the specified alpha level, as explained in De Cáceres & Legendre (2009). The combinations to be kept can be restricted to those whose positive predictive value, sensitivity and/or indicator value are equal or greater than input thresholds. Function print allows printing the results in a nice table, whereas summary provides information about candidate species, combinations and coverage of the set of indicators. Function plot draws the statistics against the order (i.e. the number of species) of the combination.
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.
Dufrêne, M. and P. Legendre. 1997. Species assemblages and indicator species: The need for a flexible asymetrical approach. Ecological Monographs 67:345-366.
predict.indicators,pruneindicators, coverage, multipatt, strassoc, signassoc
library(stats)
data(wetland) ## Loads species data
## Creates three clusters using kmeans
wetkm <- kmeans(wetland, centers=3)
## Number of sites in each group
table(wetkm$cluster)
## Run indicator analysis with species combinations for the first group
sc <- indicators(X=wetland, cluster=wetkm$cluster, group=1, verbose=TRUE,
At=0.5, Bt=0.2)
#Prints the results
print(sc)
## Plots positive predictive power and sensitivity against the order of
## combinations
plot(sc, type="A")
plot(sc, type="B")
## Run indicator analysis with species combinations for the first group,
## but forcing 'Orysp' to be in all combinations
sc2 <- indicators(X=wetland, cluster=wetkm$cluster, group=1, verbose=TRUE,
At=0.5, Bt=0.2, enableFixed=TRUE)
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