Kernel density derivative estimate for 1- to 6-dimensional data.
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x |
matrix of data values |
H,h |
bandwidth matrix/scalar bandwidth. If these are missing, |
deriv.order |
derivative order (scalar) |
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. |
deriv.vec |
flag to compute all derivatives in vectorised derivative. Default is TRUE. If FALSE then only the unique derivatives are computed. |
verbose |
flag to print out progress information. Default is FALSE. |
object |
object of class |
... |
other parameters |
For each partial derivative, for grid estimation, the estimate is a
list whose elements
correspond to the partial derivative indices in the rows of deriv.ind
.
For points estimation, the estimate is a matrix whose columns correspond to
rows of deriv.ind
.
If the bandwidth H
is missing from kdde
, then
the default bandwidth is the plug-in selector
Hpi
. Likewise for missing h
.
The effective support, binning, grid size, grid range, positive
parameters are the same as kde
.
A kernel density derivative estimate is an object of class
kdde
which is a list with fields:
x |
data points - same as input |
eval.points |
points at which the estimate is evaluated |
estimate |
density derivative 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 |
deriv.order |
derivative order (scalar) |
deriv.ind |
each row is a vector of partial derivative indices |
kde
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