| plot.bCI {assist} | R Documentation |
Bayesian Confidence Interval Plot of a Smoothing Spline Fit
Description
Create trellis plots of a nonparametric function fit together with its (approximate) 95% Bayesian confidence intervals from a ssr/slm/snr/snm object.
Usage
## S3 method for class 'bCI'
plot(x, x.val=NULL, type.name=NULL, ...)
Arguments
x |
an object of class "bCI" containing point evaluation of the unknown function and/or corresponding posterior standard devaitions. |
x.val |
an optional vector representing values of argument based on which the function is to evaluate. |
type.name |
an optional character vector specifying the names of fits. |
... |
options suitable for xyplot. |
Details
This function is to visualize a spline fit by use of trellis graphic facility with Bayesian confidence intervals superposed. Multi-panel plots, based on xyplot, are suitable for SS ANOVA decomposition of a spline estimate.
Author(s)
Chunlei Ke chunlei_ke@yahoo.com and Yuedong Wang yuedong@pstat.ucsb.edu
See Also
predict.ssr, intervals.slm,
intervals.snr, intervals.snm
Examples
## Not run:
x<- seq(0, 1, len=100)
y<- 2*sin(2*pi*x)+rnorm(x)*0.5
fit<- ssr(y~x, cubic(x))
p.fit<- predict(fit)
plot(p.fit)
plot(p.fit,type.name="fit")
## End(Not run)