kcde {ks} | R Documentation |
Kernel cumulative distribution/survival function estimate for 1- to 3-dimensional data.
kcde(x, H, h, gridsize, gridtype, xmin, xmax, supp=3.7, eval.points, binned=FALSE, bgridsize, positive=FALSE, adj.positive, w, verbose=FALSE, tail.flag="lower.tail") Hpi.kcde(x, nstage=2, pilot, Hstart, binned=FALSE, bgridsize, amise=FALSE, verbose=FALSE, optim.fun="nlm") Hpi.diag.kcde(x, nstage=2, pilot, Hstart, binned=FALSE, bgridsize, amise=FALSE, verbose=FALSE, optim.fun="nlm") hpi.kcde(x, nstage=2, binned=TRUE, amise=FALSE) ## S3 method for class 'kcde' predict(object, ..., x)
x |
matrix of data values |
H,h |
bandwidth matrix/scalar bandwidth. If these are missing, then
|
gridsize |
vector of number of grid points |
gridtype |
not yet implemented |
xmin,xmax |
vector of minimum/maximum values for grid |
supp |
effective support for standard normal |
eval.points |
points at which estimate is evaluated |
binned |
flag for binned estimation. Default is FALSE. |
bgridsize |
vector of binning grid sizes |
positive |
flag if 1-d data are positive. Default is FALSE. |
adj.positive |
adjustment applied to positive 1-d data |
w |
not yet implemented |
verbose |
flag to print out progress information. Default is FALSE. |
tail.flag |
"lower.tail" = cumulative distribution, "upper.tail" = survival function |
nstage |
number of stages in the plug-in bandwidth selector (1 or 2) |
pilot |
"dscalar" = single pilot bandwidth (default for
|
Hstart |
initial bandwidth matrix, used in numerical optimisation |
amise |
flag to return the minimal scaled PI value |
optim.fun |
|
object |
object of class |
... |
other parameters |
If tail.flag="lower.tail"
then the cumulative distribution
function Pr(X<=x) is estimated, otherwise
if tail.flag="upper.tail"
, it is the survival function
P(X>x). For d>1,
Pr(X<=x) != 1-Pr(X>x).
If the bandwidth H
is missing from kcde
, then
the default bandwidth is the plug-in selector
Hpi.kcde
. Likewise for missing h
.
No pre-scaling/pre-sphering is used since the Hpi.kcde
is not invariant to translation/dilation.
The effective support, binning, grid size, grid range, positive data
parameters are the same as for kde
.
A kernel cumulative distribution estimate is an object of class
kcde
which is a list with fields:
x |
data points - same as input |
eval.points |
points at which the estimate is evaluated |
estimate |
cumulative distribution/survival function estimate at
|
h |
scalar bandwidth (1-d only) |
H |
bandwidth matrix |
gridtype |
"linear" |
gridded |
flag for estimation on a grid |
binned |
flag for binned estimation |
names |
variable names |
w |
weights |
tail |
"lower.tail"=cumulative distribution, "upper.tail"=survival function |
Duong, T. (2014) Non-parametric kernel estimation of multivariate cumulative distribution and survival functions, and receiver operating characteristic curves. Submitted.
library(MASS) data(iris) Fhat <- kcde(iris[,1:2]) ## See other examples in ? plot.kcde