predict.ssllrm {gss}R Documentation

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 components if se.odds=TRUE.

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

Note

To supply the partial terms for partial spline models, add a component partial=I(...) in x; the "as is" function I(...) is necessary when partial has more than one column.

Author(s)

Chong Gu, chong@stat.purdue.edu

See Also

Fitting function ssllrm.


[Package gss version 1.1-0 Index]