R: Predict Method for Generalized Nonlinear Models

predict.gnm {gnm} | R Documentation

## 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

    ## S3 method for class 'gnm'
    predict(object, newdata = NULL,
    type = c("link", "response", "terms"), se.fit = FALSE, dispersion =
    NULL, terms = NULL, na.action = na.exclude, ...)

### Arguments

`object` | a fitted object of class inheriting from `"gnm"`.
`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 `"response"` is on the scale of the response variable. Thus for a default binomial model the default predictions are of log-odds (probabilities on logit scale) and `type = "response"` gives the predicted probabilities. The `"terms"` option returns a matrix giving the fitted values of each term in the model formula on the predictor scale. 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 `summary` applied to the object is used.
`terms` | with `type="terms"` by default all terms are returned. A character vector specifies which terms are to be returned
`na.action` | function determining what should be done with missing values in `newdata`. The default is to predict `NA`.
`...` | 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

    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)

[Package gnm version 1.1-2 Index]