Description Details Author(s) References Examples

Fits regression models to under- and over-dispersed count data using extended Poisson process models.

Package: | CountsEPPM |

Type: | Package |

Version: | 2.1 |

Date: | 2016-03-04 |

License: | GPL-2 |

Using Generalized Linear Model (GLM) terminology, the functions utilize linear predictors for mean and variance with log link functions to fit the regression models. Smith and Faddy (2016) gives further details about the package as well as examples of its use.

David M. Smith <smithdm1@us.ibm.com>

Faddy M, Smith D. (2011). Analysis of count data with covariate dependence in both mean and variance.
*Journal of Applied Statistics*, **38**, 2683-2694. doi: 10.1002/bimj.201100214.

Smith D, Faddy M. (2016). Mean and Variance Modeling of Under- and Overdispersed Count Data.
*Journal of Statistical Software*, **69**(6), 1-23. doi: 10.18637/jss.v069.i06.

Zeileis A, Croissant Y. (2010). Extended Model Formulas in R: Multiple Parts and Multiple Responses.
*Journal of Statistical Software*, **34**(1), 1-13. doi: 10.18637/jss.v034.i01.

1 2 3 4 5 | ```
data(herons.group)
initial <- c(1.9871533,1.9900881,3.6841305,0.4925816)
names(initial) <- c("Adult mean","Immature mean", "Variance","log(b)")
output.fn <- CountsEPPM(number.attempts~0+group | 1, herons.group,initial=initial)
print(output.fn)
``` |

```
Dependent variable is a list of frequency distributions of counts
optimization method optim:
function calls 183
convergence 0 successful
$model.type
[1] "mean and variance"
$model
[1] "general"
$covariates.matrix.mean
group Adult group Immature
1 1 0
2 0 1
attr(,"assign")
[1] 1 1
attr(,"contrasts")
attr(,"contrasts")$group
[1] "contr.treatment"
$covariates.matrix.variance
(Intercept)
1 1
2 1
attr(,"assign")
[1] 0
$offset.mean
[1] 0 0
$offset.variance
[1] 0 0
$ltvalue
[1] NA
$utvalue
[1] NA
$scale.factor.model
[1] "no"
$fixed.b
[1] NA
$estses
names.estimates. estimates se
Adult mean Adult mean 1.9871533 0.0006397077
Immature mean Immature mean 1.9900881 0.0006049773
Variance Variance 3.6841305 0.0011199579
log(b) log(b) 0.4925816 0.0001596031
$vnmax
[1] 24 25
$loglikelihood
[,1]
[1,] -120.4184
$mean.obs
[1] 7.95 6.65
$variance.obs
[1] 51.62895 34.76579
attr(,"class")
[1] "CountsEPPM"
```

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