obs_exp {dsm} R Documentation

## 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)


[Package dsm version 2.3.2 Index]