summary.addreg {addreg} | R Documentation |

## Summarizing addreg Model Fits

### Description

These functions are all `methods`

for class `addreg`

or `summary.addreg`

objects.

### Usage

```
## S3 method for class 'addreg'
summary(object, correlation = FALSE, ...)
## S3 method for class 'summary.addreg'
print(x, digits = max(3L, getOption("digits") - 3L),
signif.stars = getOption("show.signif.stars"), ...)
```

### Arguments

`object` |
an object of class |

`x` |
an object of class |

`correlation` |
logical; if |

`digits` |
the number of significant digits to use when printing. |

`signif.stars` |
logical; if |

`...` |
further arguments passed to or from other methods. |

### Details

These perform the same function as `summary.glm`

and `print.summary.glm`

,
producing similar results for `addreg`

models. `print.summary.addreg`

additionally prints
the small-sample corrected AIC (`aic.c`

), the number of EM iterations for the parameterisation
corresponding to the MLE, and for negative binomial models, the estimate of `\phi`

(`scale`

-1)
and its standard error.

The dispersion used in calculating standard errors is fixed as `1`

for binomial and Poisson
models, and is estimated via maximum likelihood for negative binomial models.

### Value

`summary.addreg`

returns an object of class `"summary.addreg"`

, a list with components

`call` |
the component from |

`family` |
the component from |

`deviance` |
the component from |

`aic` |
the component from |

`aic.c` |
the component from |

`df.residual` |
the component from |

`null.deviance` |
the component from |

`df.null` |
the component from |

`iter` |
the component from |

`deviance.resid` |
the deviance residuals: see |

`coefficients` |
the matrix of coefficients, standard errors, z-values and p-values. |

`aliased` |
included for compatibility — always |

`dispersion` |
the inferred/estimated dispersion. |

`df` |
included for compatibility — a 3-vector of the number of coefficients, the number of residual degrees of freedom, and the number of coefficients (again). |

`cov.unscaled` |
the unscaled ( |

`cov.scaled` |
ditto, scaled by |

`correlation` |
if |

For negative binomial models, the object also contains

`phi` |
the estimate of |

`var.phi` |
the estimated variance of |

### Note

If `object$boundary == TRUE`

, the standard errors of the coefficients
are not valid, and a matrix of `NaN`

s is returned by `vcov.addreg`

.

### Author(s)

Mark W. Donoghoe markdonoghoe@gmail.com

### See Also

### Examples

```
## For an example, see example(addreg)
```

*addreg*version 3.0 Index]