mvabund (version 3.6.3)

predict.manyglm: Predict Method for MANYGLM Fits

Description

Obtains predictions and optionally estimates standard errors of those predictions from a fitted manyglm object.

Usage

## S3 method for class 'manyglm':
predict(object, newdata = NULL, type = c("link", "response",
    "terms"), se.fit = FALSE, dispersion = object$theta, terms = NULL,
    na.action = na.pass, ...)

Arguments

object

a fitted object of class inheriting from "manyglm".

newdata

optionally, a 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 on the scale of the linear predictors; the alternative "response" is on the scale of the response variable. Thus for a default binomial model the default predictions are of log-

se.fit

logical switch indicating if standard errors are required.

dispersion

the dispersion of the MANYGLM 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 matrix of predictions or an array of predictions and bounds. If se = TRUE, a list with components
• fit
  the 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.

See Also

manyglm.

Examples

data(spider)
spiddat <- mvabund(spider$abund)
X <- spider$x[1:20,]

glm.spid.poiss <- manyglm(spiddat[1:20,]~X, family="poisson")

newdata <- spiddat[21:28,]
predict(glm.spid.poiss, newdata)
pred.w.plim <- predict(glm.spid.poiss, newdata, interval="prediction")
pred.w.clim <- predict(glm.spid.poiss, newdata, interval="confidence")