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Prediction based on fitted quantile regression model
# S3 method for rq
predict(object, newdata, type = "none", interval = c("none", "confidence"),
level = .95, na.action = na.pass, ...)
# S3 method for rqs
predict(object, newdata, type = "Qhat", stepfun = FALSE, na.action = na.pass, ...)
# S3 method for rq.process
predict(object, newdata, type = "Qhat", stepfun = FALSE, na.action = na.pass, ...)A vector or matrix of predictions, depending upon the setting of
'interval'. In the case that there are multiple taus in object
when object is of class 'rqs' setting 'stepfun = TRUE' will produce a
stepfun object or a list of stepfun objects.
The function rearrange can be used to monotonize these
step-functions, if desired.
object of class rq or rqs or rq.process produced by rq
An optional data frame in which to look for variables with which to predict. If omitted, the fitted values are used.
type of interval desired: default is 'none', when set to 'confidence' the function returns a matrix predictions with point predictions for each of the 'newdata' points as well as lower and upper confidence limits.
converage probability for the 'confidence' intervals.
For predict.rq, the method for 'confidence' intervals, if desired.
If 'percentile' then one of the bootstrap methods is used to generate percentile
intervals for each prediction, if 'direct' then a version of the Portnoy and Zhou
(1998) method is used, and otherwise an estimated covariance matrix for the parameter
estimates is used. Further arguments to determine the choice of bootstrap
method or covariance matrix estimate can be passed via the ... argument.
For predict.rqs and predict.rq.process when stepfun = TRUE,
type is "Qhat", "Fhat" or "fhat" depending on whether the user would
like to have estimates of the conditional quantile, distribution or density functions
respectively. As noted below the two former estimates can be monotonized with the
function rearrange. When the "fhat" option is invoked, a list of conditional
density functions is returned based on Silverman's adaptive kernel method as
implemented in akj and approxfun.
If 'TRUE' return stepfunctions otherwise return matrix of predictions.
these functions can be estimates of either the conditional quantile or distribution
functions depending upon the type argument. When stepfun = FALSE
a matrix of point estimates of the conditional quantile function at the points
specified by the newdata argument.
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.
R. Koenker
Produces predicted values, obtained by evaluating the quantile regression function in the frame 'newdata' (which defaults to 'model.frame(object)'. These predictions purport to estimate the conditional quantile function of the response variable of the fitted model evaluated at the covariate values specified in "newdata" and the quantile(s) specified by the "tau" argument. Several methods are provided to compute confidence intervals for these predictions.
Zhou, Kenneth Q. and Portnoy, Stephen L. (1998) Statistical inference on heteroscedastic models based on regression quantiles Journal of Nonparametric Statistics, 9, 239-260
rq rearrange
data(airquality)
airq <- airquality[143:145,]
f <- rq(Ozone ~ ., data=airquality)
predict(f,newdata=airq)
f <- rq(Ozone ~ ., data=airquality, tau=1:19/20)
fp <- predict(f, newdata=airq, stepfun = TRUE)
fpr <- rearrange(fp)
plot(fp[[2]],main = "Conditional Ozone Quantile Prediction")
lines(fpr[[2]], col="red")
legend(.2,20,c("raw","cooked"),lty = c(1,1),col=c("black","red"))
fp <- predict(f, newdata=airq, type = "Fhat", stepfun = TRUE)
fpr <- rearrange(fp)
plot(fp[[2]],main = "Conditional Ozone Distribution Prediction")
lines(fpr[[2]], col="red")
legend(20,.4,c("raw","cooked"),lty = c(1,1),col=c("black","red"))
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