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"
)

Arguments

overData

Matrix of functions and which species affect them from getRedundancy.

m

Number of functions. Defaults to 2.

type

Are the kinds of effects we're looking at "positive", "negative" or "all".

index

Type of overlap index to be used by getOverlap.

denom

Type of denominator to be used by getOverlap.

Value

Returns a data frame of the mean overlap, SD, and number of possible combinations.

Details

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

Author

Jarrett Byrnes.

Examples

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
#########