View source: R/getRedundancy.R
getRedundancy | R Documentation |
getRedundancy
examines which species have an effect on which function
getRedundancy( vars, species, data, negVars = NA, method = "lm", combine = "+", output = "effect", ... )
vars |
Vector of column names of functions |
species |
Vector of column names of species |
data |
data frame with species presence/absence of values of functions |
negVars |
Vector of names of species for which a negative coefficient is actually a positive effect. |
method |
Fitting function for statistical models. Defaults to |
combine |
How are species combined in the model? Defaults to "+" for additive combinations. |
output |
Will the output be sign of effect or "coefficient". Defaults to "effect" |
... |
Other arguments to be supplied to fitting function. |
getRedundancy takes a matrix of 1s,0s, 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 returns the overlap index for each combination. For species whose effect is not different from 0 at the alpha=0.05 level, a 0 is returned.
Returns a matrix of functions and the effect of species on each. 1s, -1s, and 0s for "effect" or coefficients.
Jarrett Byrnes.
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 getRedundancy(vars, species, germany) getRedundancy(vars, species, germany, output = "coef") ######### # takes a vector of responses, the species that may cause them # and returns a table of 1s, -1s, and 0s with regards to the kind of effect # or a coefficient table, if asked for. Arugments can take the form of the fitting function # how variables are combined, and additional arguments to the fitting function #########
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