predict.vglm: Predict Method for a VGLM fit

Description

Predicted values based on a vector generalized linear model (VGLM) object.

Usage

predict.vglm(object, newdata = NULL, 
             type = c("link", "response", "terms"), 
             se.fit = FALSE, deriv = 0, dispersion = NULL,
             untransform=FALSE, extra = object@extra, ...)

Arguments

object

Object of class inheriting from "vlm".

newdata

An optional data frame in which to look for variables with which to predict. If omitted, the fitted linear predictors are used.

type

the type of prediction required. The default is the first one, meaning on the scale of the linear predictors. The alternative "response" is on the scale of the response variable, and depending on the family function, this may or may no

se.fit

logical: return standard errors?

deriv

Non-negative integer. Currently this must be zero. Later, this may be implemented for general values.

dispersion

Dispersion parameter. This may be inputted at this stage, but the default is to use the dispersion parameter of the fitted model.

extra

A list containing extra information. This argument should be ignored.

untransform

Logical. Reverses any parameter link function. This argument only works if type="link", se.fit=FALSE, deriv=0.

...

Arguments passed into predict.vlm.

Value

If se.fit = FALSE, a vector or matrix of predictions. If se.fit = TRUE, a list with components
fitted.valuesPredictions
se.fitEstimated standard errors
dfDegrees of freedom
sigmaThe square root of the dispersion parameter

Warning

This function may change in the future.

Details

Obtains predictions and optionally estimates standard errors of those predictions from a fitted vector generalized linear model (VGLM) object.

This code implements smart prediction (see smartpred).

References

Yee, T. W. and Hastie, T. J. (2003) Reduced-rank vector generalized linear models. Statistical Modelling, 3, 15--41.

Examples

Run this code

# Illustrates smart prediction
data(pneumo)
pneumo = transform(pneumo, let=log(exposure.time))
fit = vglm(cbind(normal,mild, severe) ~ poly(c(scale(let)), 2),
           fam=cumulative(parallel=TRUE),
           data=pneumo, trace=TRUE, x=FALSE)
class(fit)

(q0 = predict(fit)[1:3,])
(q1 = predict(fit, newdata=pneumo)[1:3,])
(q2 = predict(fit, newdata=pneumo[1:3,]))
all.equal(q0, q1)  # Should be TRUE
all.equal(q1, q2)  # Should be TRUE

predict(fit)[1:3,]
predict(fit, untransform=TRUE)[1:3,]

p0 = predict(fit, type="res")[1:3,]
p1 = predict(fit, type="res", newdata=pneumo)[1:3,]
p2 = predict(fit, type="res", newdata=pneumo[1:3,])
p3 = fitted(fit)[1:3,]
all.equal(p0, p1)  # Should be TRUE
all.equal(p1, p2)  # Should be TRUE
all.equal(p2, p3)  # Should be TRUE

predict(fit, type="t", se=TRUE)

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