predict.ssllrm: Evaluating Log-Linear Regression Model Fits

In gss: General Smoothing Splines

Evaluating Log-Linear Regression Model Fits

Description

Evaluate conditional density in a log-linear regression model fit at arbitrary x, or contrast of log conditional density possibly with standard errors for constructing Bayesian confidence intervals.

Usage

## S3 method for class 'ssllrm'
predict(object, x, y=object$qd.pt, odds=NULL, se.odds=FALSE, ...)

Arguments

object	Object of class "ssllrm".
x	Data frame of x values.
y	Data frame of y values; y-variables must be factors.
odds	Optional coefficients of contrast.
se.odds	Flag indicating if standard errors are required. Ignored when odds=NULL.
...	Ignored.

Value

For odds=NULL, predict.ssanova returns a vector/matrix of the estimated f(y|x).

When odds is given, it should match y in length and the coefficients must add to zero; predict.ssanova then returns a vector of estimated "odds ratios" if se.odds=FALSE or a list consisting of the following elements if se.odds=TRUE.

fit	Vector of evaluated fit.
se.fit	Vector of standard errors.

See Also

Fitting function ssllrm.

gss documentation built on Aug. 16, 2023, 9:07 a.m.