getOverlapSummary summarizes the number of species necessary for each function
including means, SDs, and other metrics
getOverlapSummary(
overData,
m = 2,
type = "positive",
index = "sorensen",
denom = "set"
)Matrix of functions and which species affect them from getRedundancy.
Number of functions. Defaults to 2.
Are the kinds of effects we're looking at "positive", "negative" or "all".
Type of overlap index to be used by getOverlap.
Type of denominator to be used by getOverlap.
Returns a data frame of the mean overlap, SD, and number of possible combinations.
getOverlapSummary takes a matrix of 1s and -1s, and depending on whether we're interested in positive, negative, or both types of interactions looks for the m-wise overlap between species and then reports summary metrics of mean overlap, SD, and number of combinations
data(all_biodepth)
allVars <- qw(biomassY3, root3, N.g.m2, light3, N.Soil, wood3, cotton3)
germany <- subset(all_biodepth, all_biodepth$location == "Germany")
vars <- whichVars(germany, allVars)
species <- relevantSp(germany, 26:ncol(germany))
# re-normalize N.Soil so that everything is on the same
# sign-scale (e.g. the maximum level of a function is
# the "best" function)
germany$N.Soil <- -1 * germany$N.Soil + max(germany$N.Soil, na.rm = TRUE)
res.list <- lapply(vars, function(x) sAICfun(x, species, germany))
names(res.list) <- vars
redund <- getRedundancy(vars, species, germany)
getOverlapSummary(redund, m = 2)
#> meanOverlap sdOverlap n
#> 0.2827506 0.2055547 10.0000000
#########
# getOverlapSummary takes a matrix of 1s and -1s, and depending on whether we're
# interested in positive, negative, or both types of interactions looks for the
# m-wise overlap and then reports summary metrics of mean overlap, SD, and number of combinations
#########