obs_exp: Observed versus expected diagnostics for fitted DSMs

In dsm: Density Surface Modelling of Distance Sampling Data

Observed versus expected diagnostics for fitted DSMs

Description

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.

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.

Value

data.frame with values of observed and expected counts.

Author(s)

David L Miller, on the suggestion of Mark Bravington.

Examples

## Not run: 
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)

## End(Not run)

dsm documentation built on Aug. 21, 2022, 1:07 a.m.