qrjoint (version 2.0-9)

predict.qrjoint: Posterior predictive summary for quantile estimation

Description

Extract quantile functions for qrjoint

Usage

# S3 method for qrjoint
predict(object, newdata=NULL, summarize=TRUE, burn.perc = 0.5, nmc = 200, ...)

Value

Either returns a matrix of posterior quantile-function estimates if summarize=TRUE. Dimensions are n (number of rows in predicted data) x L (length of regularized tau.grid); or a three dimensional array of posterior quantile-function samples if summarize=FALSE. Dimensions are n (number of rows in predicted data) x L (length of regularized tau.grid) x nmc (retained posterior draws).

Arguments

object

a fitted model of the class 'qrjoint'.

newdata

an optional data frame containing variables on which to predict. If omitted, the fitted data are used.

summarize

a logical indicating whether the quantile functions should be summarized across posterior draws into a single estimate (TRUE) or be left as individual samples (FALSE)

burn.perc

a positive fraction indicating what fraction of the saved draws are to be discarded as burn-in

nmc

integer giving the number of samples, post burn-in, to be used in Monte Carlo averaging

...

currently no additional arguments are allowed

See Also

qrjoint and summary.qrjoint.

Examples

## Plasma data analysis

# recoding variables
data(plasma)
plasma$Sex <- as.factor(plasma$Sex)
plasma$SmokStat <- as.factor(plasma$SmokStat)
plasma$VitUse <- 3 - plasma$VitUse
plasma$VitUse <- as.factor(plasma$VitUse)

# Model fitting with 40 posterior samples from 80 iterations (thin = 2) is for
# illustration only. For practical model fitting, increase iterations, 
# e.g. nsamp = 500, thin = 20
if (FALSE) {
fit.qrj <- qrjoint(BetaPlasma ~ Age + Sex + SmokStat + Quetelet + VitUse + Calories + 
        Fat + Fiber + Alcohol + Cholesterol + BetaDiet, plasma, nsamp = 40, thin = 2)

quants <- predict(fit.qrj)
matplot(fit.qrj$tau.g[fit.qrj$reg.ix], t(quants), type="l", xlab="p",
ylab="Quantile Function", col="lightgray", lty=1)
}