multivariate: Nonparametric Multivariate Probability Distributions

In empirical: Probability Distributions as Models of Data

Description

Empirical multivariate probability density functions and empirical multivariate cumulative distribution functions. Refer to the vignette for better examples.

Usage

epdfmv (x,
    restack.pdf=sbcpdf, restack.cdf=sbccdf,
    rsp=0.5, bw, bind=TRUE, w=NA)
ecdfmv (x,
    restack.pdf=sbcpdf, restack.cdf=sbccdf,
    rsp=0.5, bw, bind=TRUE, w=NA)

Arguments

x	A vector of data points.
restack.pdf	A PDF used for restacking. ECDFs ignore this.
restack.cdf	A CDF used for restacking. EPDFs only use this if plotting bivariate models with all=TRUE.
rsp	A restacking parameter. Refer to the vignette.
bw	A bandwidth parameter. Refer to the vignette.
bind	If true, add an extra two data points.
w	A vector of weights.

Value

These functions return functions.

See Also

epdfuv, ecdfuv, ecdfuv.inverse, epdfc, ecdfc, ecdfc.inverse, epdfmv.f, ecdfmv.f

Examples

#construct an empirical multivariate probability density function
#and then evaluate it
data (trees)
attach (trees)
epdfmv.f = epdfmv (cbind (Height, Volume) )
epdfmv.f (c (80, 30) )

empirical documentation built on Dec. 3, 2018, 1:04 a.m.