conditional: Nonparametric Conditional Probability Distributions

In empirical: Probability Distributions as Models of Data

Description

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

Usage

epdfc (rv, conditions, x,
    restack.pdf=sbcpdf, rsp=0.5, bw,
    bind=TRUE, w=NA, is.string=FALSE, npoints=30)
ecdfc (rv, conditions, x,
    restack.pdf=sbcpdf, rsp=0.5, bw,
    bind=TRUE, w=NA, is.string=FALSE, npoints=30)
ecdfc.inverse (rv, conditions, x,
    restack.pdf=sbcpdf, rsp=0.5, bw,
    bind=TRUE, w=NA, is.string=FALSE, npoints=30)

Arguments

rv	A name of a random variable.
conditions	A named vector of conditions.
x	A vector of data points.
restack.pdf	A PDF used for restacking.
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.
is.string	If true rv is expected to be a character type, otherwise, rv is expected to be an unquoted name of a variable.
npoints	Number of synthetic data points.

Value

These functions return functions.

See Also

epdfuv, ecdfuv, ecdfuv.inverse, epdfmv, ecdfmv

Examples

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

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