Given a covariate, calculate the observed and expected counts for each
unique value of the covariate. This can be a useful goodness of fit check
for DSMs.
Usage
obs_exp(model, covar, cut = NULL)
Arguments
- model
a fitted dsm model object
- covar
covariate to aggregate by (character)
- cut
vector of cut points to aggregate at. If not supplied, the unique
values of covar are used.
Value
data.frame with values of observed and expected counts.
Details
One strategy for model checking is to calculate observed and expected counts
at different aggregations of the variable. If these match well then the
model fit is good.
Author
David L Miller, on the suggestion of Mark Bravington.
Examples
if (FALSE) { # \dontrun{
library(Distance)
library(dsm)
# example with the Gulf of Mexico dolphin data
data(mexdolphins)
hr.model <- ds(distdata, truncation=6000,
key = "hr", adjustment = NULL)
mod1 <- dsm(count~s(x,y), hr.model, segdata, obsdata)
} # }