kde2dnew.fortran: A 2-d normal kernel estimator

Description Usage Arguments Details Value Author(s) References

View source: R/kde2dnew.fortran.R

Description

A simple and quick 2-d weighted normal kernel estimator, with fixed bandwidth and relative integral.

Usage

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kde2dnew.fortran(xkern, ykern, gx, gy, h, factor.xy = 1,eps=0, w =
 replicate(length(xkern), 1)

)

kde2d.integral(xkern, ykern, gx = xkern, gy = ykern, eps = 0, factor.xy = 1, 
h = c(bwd.nrd(xkern, w), bwd.nrd(ykern, w)),
w = replicate(length(xkern), 1),wmat=numeric(0)

)

Arguments

xkern

x-values of kernel points of length n (n=length(xkern)).

ykern

y-values of kernel points of length n.

gx

x-values of the points where densities must be estimated.

gy

y-values of the points where densities must be estimated.

h

bandwidths: a length 2 numerical vector.

factor.xy

expansion factor for bandwidths (density will be smoother if factor.xy>1).

w

vector of weights to give to observed points (length n).

wmat

if kern.var=TRUE defines the variable metric

eps

enlargment factor for the region of interest.

Details

A standard bivariate normal kernel estimator.

Value

grid values and estimated densities.

Author(s)

Marcello Chiodi.

References

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer. Wand, M.P and Jones, M.C. (1995). Kernel Smoothing. London: Chapman & Hall/CRC.


etasFLP documentation built on May 1, 2019, 6:48 p.m.