predict.sscox: Evaluating Smoothing Spline ANOVA Estimate of Relative Risk

Description Usage Arguments Value Note Author(s) See Also

Description

Evaluate terms in a smoothing spline ANOVA estimate of relative risk at arbitrary points. Standard errors of the terms can be requested for use in constructing Bayesian confidence intervals.

Usage

1
2
3
## S3 method for class 'sscox'
predict(object, newdata, se.fit=FALSE,
                        include=c(object$terms$labels,object$lab.p), ...)

Arguments

object

Object of class "sscox".

newdata

Data frame or model frame in which to predict.

se.fit

Flag indicating if standard errors are required.

include

List of model terms to be included in the prediction.

...

Ignored.

Value

For se.fit=FALSE, predict.sscox returns a vector of the evaluated relative risk.

For se.fit=TRUE, predict.sscox returns a list consisting of the following components.

fit

Vector of evaluated relative risk.

se.fit

Vector of standard errors for log relative risk.

Note

For mixed-effect models through sscox, the Z matrix is set to 0 if not supplied. To supply the Z matrix, add a component random=I(...) in newdata, where the as-is function I(...) preserves the integrity of the Z matrix in data frame.

Author(s)

Chong Gu, chong@stat.purdue.edu

See Also

Fitting functions sscox and method project.sscox.



Search within the gss package
Search all R packages, documentation and source code

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.