# NOT RUN {
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)
filterOverData(redund, type = "positive")
#########
# filterOverData takes a matrix of 1s, 0s, and -1s
# and filters it so that only the positive, negative, or both contributions
# are 1 for later overlap function usage
#########
# }
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