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.
Arguments
- model
fitted model
- type
concurvity measure to plot, see
concurvity
Details
These methods are considered somewhat experimental at this time. Consult
concurvity for more information on how concurvity
measures are calculated.
Examples
if (FALSE) { # \dontrun{
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, truncation=6000,
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)
} # }