## loomis

### Description

Data are taken from Loomis (2003). The study relates to a survey taken on reported
frequency of visits to national parks during the year. The survey was taken at park
sites, thus incurring possible effects of endogenous stratification.

### Usage

`data(loomis)`

### Format

A data frame with 410 observations on the following 11 variables.

`anvisits`

number of annual visits to park

`gender`

1=male;0=female

`income`

income in US dollars per year, categorical: 4 levels

`income1`

<=$25000

`income2`

>$25000 - $55000

`income3`

>$55000 - $95000

`income4`

>$95000

`travel`

travel time, categorical: 3 levels

`travel1`

<.25 hrs

`travel2`

>=.25 - <4 hrs

`travel3`

>=4 hrs

### Details

loomis is saved as a data frame.
Count models typically use anvisits as response variable. 0 counts are included

### Source

from Loomis (2003)

### References

Hilbe, Joseph M (2007, 2011), Negative Binomial Regression, Cambridge University Press
Loomis, J. B. (2003). Travel cost demand model based river recreation benefit
estimates with on-site and household surveys: Comparative results and a
correction procedure, Water Resources Research, 39(4): 1105

### Examples

```
data(loomis)
glmlmp <- glm(anvisits ~ gender + factor(income) + factor(travel), family=poisson, data=loomis)
summary(glmlmp)
exp(coef(glmlmp))
library(MASS)
glmlmnb <- glm.nb(anvisits ~ gender + factor(income) + factor(travel), data=loomis)
summary(glmlmnb)
exp(coef(glmlmnb))
```