obs_exp {dsm} | R Documentation |
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.
obs_exp(model, covar, cut = NULL)
model |
a fitted |
covar |
covariate to aggregate by (character) |
cut |
vector of cut points to aggregate at. If not supplied, the unique values of |
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.
data.frame
with values of observed and expected counts.
David L Miller, on the suggestion of Mark Bravington.
library(Distance) library(dsm) # example with the Gulf of Mexico dolphin data data(mexdolphins) hr.model <- ds(distdata, max(distdata$distance), key = "hr", adjustment = NULL) mod1 <- dsm(count~s(x,y), hr.model, segdata, obsdata)