predict.betareg {betareg} | R Documentation |
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.
## S3 method for class 'betareg' predict(object, newdata = NULL, type = c("response", "link", "precision", "variance", "parameter", "density", "probability", "quantile"), na.action = na.pass, at = 0.5, ...)
object |
fitted model object of class |
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 ( |
na.action |
function determining what should be done with missing values
in |
at |
numeric vector indicating the level(s) at which quantiles
should be predicted (only if |
... |
currently not used. |
FIXME: Update to extended type and at processing.
FIXME: Add comments about pit and rootogram.
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)))