kde {ks} | R Documentation |
Kernel density estimate for 1- to 6-dimensional data.
kde(x, H, h, gridsize, gridtype, xmin, xmax, supp=3.7, eval.points, binned=FALSE, bgridsize, positive=FALSE, adj.positive, w, compute.cont=FALSE, approx.cont=TRUE, unit.interval=FALSE, verbose=FALSE) ## S3 method for class 'kde' predict(object, ..., x)
x |
matrix of data values |
H,h |
bandwidth matrix/scalar bandwidth. If these are missing, |
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 |
vector of weights. Default is a vector of all ones. |
compute.cont |
flag for computing 1% to 99% probability contour levels. Default is FALSE. |
approx.cont |
flag for computing approximate probability contour levels. Default is TRUE. |
unit.interval |
flag if 1-d data are bounded on unit interval [0,1]. Default is FALSE. |
verbose |
flag to print out progress information. Default is FALSE. |
object |
object of class |
... |
other parameters |
For d=1, if h
is missing, the default bandwidth is hpi
.
For d>1, if H
is missing, the default is Hpi
.
For d=1, 2, 3, 4, and if eval.points
is not specified, then the
density estimate is computed over a grid
defined by gridsize
(if binned=FALSE
) or
by bgridsize
(if binned=TRUE
).
If eval.points
is specified, then the
density estimate is computed exactly at eval.points
.
For d>4, the kernel density estimate is computed exactly
and eval.points
must be specified.
For d=1, if positive=TRUE
then x<-log(x+adj.positive)
where the default adj.positive
is the minimum of x
.
The effective support for a normal kernel is supp
, i.e.
all values outside [-supp,supp]^d
are set to zero.
The default xmin
is min(x)-Hmax*supp
and xmax
is max(x)+Hmax*supp
where Hmax
is the maximum of the
diagonal elements of H
. The grid produced is the outer
product of [xmin[1], xmax[1]]
, ..., [xmin[d], xmax[d]]
.
The default bgridsize, gridsize
are d=1: 401; d=2: rep(151, 2);
d=3: rep(31, 3); d=4: rep(21,4).
A kernel density estimate is an object of class kde
which is a
list with fields:
x |
data points - same as input |
eval.points |
points at which the estimate is evaluated |
estimate |
density 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 |
cont |
probability contour levels (if |
## positive data example set.seed(8192) x <- 2^rnorm(100) fhat <- kde(x=x, positive=TRUE) plot(fhat) points(c(0.5, 1), predict(fhat, x=c(0.5, 1))) ## See other examples in ? plot.kde