# Predict Method for Generalized Nonlinear Models

### Description

Obtains predictions and optionally estimates standard errors of those predictions from a fitted generalized nonlinear model object.

### Usage

1 2 3 4 |

### Arguments

`object` |
a fitted object of class inheriting from |

`newdata` |
optionally, a data frame in which to look for variables with which to predict. If omitted, the fitted predictors are used. |

`type` |
the type of prediction required. The default is on the scale
of the predictors; the alternative The value of this argument can be abbreviated. |

`se.fit` |
logical switch indicating if standard errors are required. |

`dispersion` |
the dispersion of the fit to be assumed in computing
the standard errors. If omitted, that returned by |

`terms` |
with |

`na.action` |
function determining what should be done with missing values
in |

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

### Details

If `newdata`

is omitted the predictions are based on the data used
for the fit. In that case how cases with missing values in the
original fit is determined by the `na.action`

argument of that
fit. If `na.action = na.omit`

omitted cases will not appear in
the residuals, whereas if `na.action = na.exclude`

they will
appear (in predictions and standard errors), with residual value
`NA`

. See also `napredict`

.

### Value

If `se = FALSE`

, a vector or matrix of predictions. If ```
se =
TRUE
```

, a list with components

` fit ` |
predictions. |

` se.fit ` |
estimated standard errors. |

` residual.scale ` |
a scalar giving the square root of the dispersion used in computing the standard errors. |

### Note

Variables are first looked for in 'newdata' and then searched for in the usual way (which will include the environment of the formula used in the fit). A warning will be given if the variables found are not of the same length as those in 'newdata' if it was supplied.

### Author(s)

Heather Turner

### References

Chambers, J. M. and Hastie, T. J. (1992) *Statistical Models in S *

### See Also

`gnm`

### Examples

1 2 3 4 5 6 7 8 9 10 11 12 | ```
set.seed(1)
## Fit an association model with homogeneous row-column effects
RChomog <- gnm(Freq ~ origin + destination + Diag(origin, destination) +
MultHomog(origin, destination), family = poisson,
data = occupationalStatus)
## Fitted values (expected counts)
predict(RChomog, type = "response", se.fit = TRUE)
## Fitted values on log scale
predict(RChomog, type = "link", se.fit = TRUE)
``` |