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$distance), key = "hr", adjustment = NULL) # fit a simple smooth of x and y to counts mod1 <- dsm(count~s(x,y)+s(depth), hr.model, segdata, obsdata) # visualise concurvity using the "estimate" metric vis.concurvity(mod1) ## End(Not run)
Loading required package: mgcv Loading required package: nlme This is mgcv 1.8-33. For overview type 'help("mgcv-package")'. Loading required package: mrds This is mrds 2.2.3 Built: R 4.0.3; ; 2020-11-21 12:02:24 UTC; unix Loading required package: numDeriv This is dsm 2.3.0 Built: R 4.0.3; ; 2020-11-24 09:14:04 UTC; unix Attaching package: ‘Distance’ The following object is masked from ‘package:mrds’: create.bins Fitting hazard-rate key function Key only model: not constraining for monotonicity. AIC= 841.253 No survey area information supplied, only estimating detection function. Warning message: In ds(distdata, max(distdata$distance), key = "hr", adjustment = NULL) : Estimated hazard-rate scale parameter close to 0 (on log scale). Possible problem in data (e.g., spike near zero distance).
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