divNeeded {multifunc}

divNeeded

Description

divNeeded Determines, for every combination of functions, how many species influence those functions.

Usage

divNeeded(overData, type = "positive")

Arguments

overData

Matrix of functions and which species affect them from getRedundancy.

type

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

Details

Iterates over all possible combinations of functions. Checks the matrix of which species have positive, negative, or both influences on those functions. Tally's total number of species that have an effect on those functions

Value

Returns a data frame of all combinations and how many species are needed to influence all of them.

Author(s)

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)

posCurve <- divNeeded(redund, type = "positive")