Predict from fitted nonparametric quantile regression smoothing spline models
Additive models for nonparametric quantile regression using total
variation penalty methods can be fit with the rqss
function. Univarariate and bivariate components can be predicted
using these functions.
## S3 method for class 'rqss' predict(object, newdata, interval = "none", level = 0.95, ...) ## S3 method for class 'qss1' predict(object, newdata, ...) ## S3 method for class 'qss2' predict(object, newdata, ...)
object |
is a fitted object produced by |
newdata |
a data frame describing the observations at which prediction is to be made. For qss components, newdata should lie in strictly within the convex hull of the fitting data. Newdata corresponding to the partially linear component of the model may require caution concerning the treatment of factor levels, if any. |
interval |
If set to |
level |
intended coverage probability for the confidence intervals |
... |
optional arguments |
For both univariate and bivariate prediction linear interpolation is
done. In the bivariate case, this involves computing barycentric
coordinates of the new points relative to their enclosing triangles.
It may be of interest to plot individual components of fitted rqss
models: this is usually best done by fixing the values of other
covariates at reference values typical of the sample data and
predicting the response at varying values of one qss term at a
time. Direct use of the predict.qss1 and predict.qss2 functions
is discouraged since it usually corresponds to predicted values
at absurd reference values of the other covariates, i.e. zero.
A vector of predictions, or in the case that interval = "confidence")
a matrix whose first column is the vector of predictions and whose second and
third columns are the lower and upper confidence limits for each prediction.
R. Koenker
n <- 200
lam <- 2
x <- sort(rchisq(n,4))
z <- exp(rnorm(n)) + x
y <- log(x)+ .1*(log(x))^2 + z/4 + log(x)*rnorm(n)/4
plot(x,y - z/4 + mean(z)/4)
Ifit <- rqss(y ~ qss(x,constraint="I") + z)
sfit <- rqss(y ~ qss(x,lambda = lam) + z)
xz <- data.frame(z = mean(z),
x = seq(min(x)+.01,max(x)-.01,by=.25))
lines(xz[["x"]], predict(Ifit, xz), col=2)
lines(xz[["x"]], predict(sfit, xz), col=3)
legend(10,2,c("Increasing","Smooth"),lty = 1, col = c(2,3))
title("Predicted Median Response at Mean Value of z")
## Bivariate example -- loads pkg "tripack"
require(tripack)
require(akima)
data(CobarOre)
fit <- rqss(z ~ qss(cbind(x,y), lambda=.08),
data= CobarOre)
plot(fit, col="grey",
main = "CobarOre data -- rqss(z ~ qss(cbind(x,y)))")
T <- with(CobarOre, tri.mesh(x, y))
set.seed(77)
ndum <- 100
xd <- with(CobarOre, runif(ndum, min(x), max(x)))
yd <- with(CobarOre, runif(ndum, min(y), max(y)))
table(s <- in.convex.hull(T, xd, yd))
pred <- predict(fit, data.frame(x = xd[s], y = yd[s]))
contour(interp(xd[s],yd[s], pred),
col="red", add = TRUE)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.