Plot measures of how much one term in the model could be explained by another. When values are high, one should consider re-running variable selection with one of the offending variables removed to check for stability in term selection.
concurvity measure to plot, see
These methods are considered somewhat experimental at this time. Consult
concurvity for more information on how concurvity measures are calculated.
David L Miller
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## Not run: library(Distance) library(dsm) # load the Gulf of Mexico dolphin data (see ?mexdolphins) data(mexdolphins) # fit a detection function and look at the summary hr.model <- ds(distdata, max(distdata$distance), key = "hr", adjustment = NULL) # fit a simple smooth of x and y to counts mod1 <- dsm(count~s(x,y)+s(depth), hr.model, segdata, obsdata) # visualise concurvity using the "estimate" metric vis.concurvity(mod1) ## End(Not run)
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