kqr
performs
non-parametric Quantile Regression.## S3 method for class 'formula':
kqr(x, data=NULL, ..., subset, na.action = na.omit, scaled = TRUE)## S3 method for class 'vector':
kqr(x,...)
## S3 method for class 'matrix':
kqr(x, y, scaled = TRUE, tau = 0.5, C = 0.1, kernel = "rbfdot", kpar = "automatic",
reduced = FALSE, rank = dim(x)[1]/6, fit = TRUE, cross = 0, na.action = na.omit)
## S3 method for class 'kernelMatrix':
kqr(x, y, tau = 0.5, C = 0.1, fit = TRUE, cross = 0)
## S3 method for class 'list':
kqr(x, y, tau = 0.5, C = 0.1, kernel = "strigdot", kpar= list(length=4, C=0.5),
fit = TRUE, cross = 0)
kernelMatrix
of the training data or a list of character vectorkqr
is called from.scaled
is of length 1, the value is recycled as
many times as needed and all non-binary variables are scaled.
Per default, data are scaled internally (both x
kernlab
provides the most popular kernel functions
which can sigma
inverse kernel width for the Radial Basis
kqr
with large datasets since normally an nreduced
is TRUE
(default :
dim(x)[1]/6)NA
s are
found. The default action is na.omit
, which leads to
rejection of cases with missing values on any required variable. An
alternative is na.fail
, which kqr
containing the fitted model along with
information.Accessor functions can be used to access the slots of the
object which include :coef
.kqr-class
for more details.ipop
implemented in kernlab
.predict.kqr
, kqr-class
, ipop
, rvm
, ksvm
# create data
x <- sort(runif(300))
y <- sin(pi*x) + rnorm(300,0,sd=exp(sin(2*pi*x)))
# first calculate the median
qrm <- kqr(x, y, tau = 0.5, C=0.15)
# predict and plot
plot(x, y)
ytest <- predict(qrm, x)
lines(x, ytest, col="blue")
# calculate 0.9 quantile
qrm <- kqr(x, y, tau = 0.9, kernel = "rbfdot", kpar= list(sigma=10), C=0.15)
ytest <- predict(qrm, x)
lines(x, ytest, col="red")
# calculate 0.1 quantile
qrm <- kqr(x, y, tau = 0.1,C=0.15)
ytest <- predict(qrm, x)
lines(x, ytest, col="green")
# print first 10 model coefficients
coef(qrm)[1:10]
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