View source: R/getOverlapSummary.R
getOverlapSummary | R Documentation |
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" )
overData |
Matrix of functions and which species affect them from |
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 |
denom |
Type of denominator to be used by |
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
Returns a data frame of the mean overlap, SD, and number of possible combinations.
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 redund <- getRedundancy(vars, species, germany) getOverlapSummary(redund, m = 2) ######### # 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 #########
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.