Kernel cumulative distribution/survival function estimate for 1 to 3dimensional data.
1 2 3 4 5 6 7 8 9 10 11  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 1d data are positive. Default is FALSE. 
adj.positive 
adjustment applied to positive 1d 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 plugin 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 
optimiser function: one of 
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) != 1Pr(X>x).
If the bandwidth H
is missing in kcde
, then
the default bandwidth is the plugin selector
Hpi.kcde
. Likewise for missing h
.
No prescaling/presphering is used since the Hpi.kcde
is not invariant to translation/dilation.
The effective support, binning, grid size, grid range, positive
parameters are the same as 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 (1d 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. (2015) Nonparametric smoothed estimation of multivariate cumulative distribution and survival functions, and receiver operating characteristic curves. Journal of the Korean Statistical Society. In press. DOI:10.1016/j.jkss.2015.06.002.
kde
, plot.kcde
1 2 3 4 5 6 
Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.
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