vis.concurvity {dsm} | R Documentation |
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.
vis.concurvity(model, type = "estimate")
model |
fitted model |
type |
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
## 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)