cdsscden: Evaluating Conditional PDF, CDF, and Quantiles of Smoothing...

In gss: General Smoothing Splines

Evaluating Conditional PDF, CDF, and Quantiles of Smoothing Spline Conditional Density Estimates

Description

Evaluate conditional pdf, cdf, and quantiles of f(y1|x,y2) for smoothing spline conditional density estimates f(y|x).

Usage

cdsscden(object, y, x, cond, int=NULL)
cpsscden(object, q, x, cond)
cqsscden(object, p, x, cond)

Arguments

object	Object of class "sscden" or "sscden1".
x	Data frame of x values on which conditional density f(y1|x,y2) is to be evaluated.
y	Data frame or vector of y1 points on which conditional density f(y1|x,y2) is to be evaluated.
cond	One row data frame of conditioning variables y2.
q	Vector of points on which cdf is to be evaluated.
p	Vector of probabilities for which quantiles are to be calculated.
int	Vector of normalizing constants.

Details

The arguments x and y are of the same form as the argument newdata in predict.lm, but y in cdsscden can take a vector for 1-D y1.

cpsscden and cqsscden naturally only work for 1-D y1.

Value

cdsscden returns a list object with the following elements.

pdf	Matrix or vector of conditional pdf f(y1|x,y2), with each column corresponding to a distinct x value.
int	Vector of normalizing constants.

cpsscden and cqsscden return a matrix or vector of conditional cdf or quantiles of f(y1|x,y2).

Note

If variables other than factors or numerical vectors are involved in y1, the normalizing constants can not be computed.

See Also

Fitting function sscden and dsscden.

gss documentation built on Oct. 12, 2024, 1:08 a.m.