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
#########