gnm (version 0.9-9)

predict.gnm: 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

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.

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.

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.

References

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

See Also

gnm

Examples

set.seed(1)
data(occupationalStatus)

##  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)