predict.betareg {betareg}

Prediction Method for betareg Objects

Description

Extract various types of predictions from beta regression models: either on the scale of responses in (0, 1) or the scale of the linear predictor.

Usage

## S3 method for class 'betareg'
predict(object, newdata = NULL,
  type = c("response", "link", "precision", "variance", "parameters",
    "density", "probability", "quantile"),
  na.action = na.pass, at = 0.5, ...)

Arguments

object

fitted model object of class "betareg".

newdata

optionally, a data frame in which to look for variables with which to predict. If omitted, the original observations are used.

type

character indicating type of predictions: fitted means of response ("response"), corresponding linear predictor ("link"), fitted precision parameter phi ("precision"), fitted variances of response ("variance"), or fitted quantile(s) of the response distribution ("quantile").

na.action

function determining what should be done with missing values in newdata. The default is to predict NA.

at

numeric vector indicating the level(s) at which quantiles should be predicted (only if type = "quantile"), defaulting to the median at = 0.5.

...

currently not used.

Details

FIXME: Update to extended type and at processing.

FIXME: Add comments about pit and rootogram.

Examples

options(digits = 4)

data("GasolineYield", package = "betareg")

gy2 <- betareg(yield ~ batch + temp | temp, data = GasolineYield)

cbind(
  predict(gy2, type = "response"),
  predict(gy2, type = "link"),
  predict(gy2, type = "precision"),
  predict(gy2, type = "variance"),
  predict(gy2, type = "quantile", at = c(0.25, 0.5, 0.75))
)

## evaluate cumulative _p_robabilities for (small) new data set
gyd <- GasolineYield[c(1, 5, 10), ]
## CDF at 0.1 for each observation
predict(gy2, newdata = gyd, type = "probability", at = 0.1)
## CDF at each combination of 0.1/0.2 and observations
predict(gy2, newdata = gyd, type = "probability", at = c(0.1, 0.2))
## CDF at pairwise combinations of 0.1/0.2/0.3 and observations
predict(gy2, newdata = gyd, type = "probability", at = c(0.1, 0.2, 0.3))
## CDF at all combinations of 0.1/0.2/0.3 and observations
predict(gy2, newdata = gyd, type = "probability", at = rbind(c(0.1, 0.2, 0.3)))