ci.plot
Plot confidence and prediction intervals for simple linear regression
The data, the least squares line, the confidence interval lines, and the
prediction interval lines for a simple
linear regression (lm(y ~ x)
) are displayed. Tick marks are
placed at the location of xbar, the x-value of the narrowest interval.
- Keywords
- regression, hplot
Usage
ci.plot(lm.object, ...)# S3 method for lm
ci.plot(lm.object,
xlim=range(data[, x.name]),
newdata,
conf.level=.95,
data=model.frame(lm.object),
newfit,
ylim,
pch=19,
lty=c(1,3,4,2),
lwd=2,
main.cex=1,
main=list(paste(100*conf.level,
"% confidence and prediction intervals for ",
substitute(lm.object), sep=""), cex=main.cex), ...
)
Arguments
- lm.object
Linear model for one
y
and onex
variable.- xlim
xlim
for plot. Default is based on data from whichlm.object
was constructed.- newdata
data.frame
containing data for which predictions are wanted. The variable name of the column must be identical to the name of the predictor variable in the model object. Defaults to a data.frame containing a vector spanning the range of observed data. User-specified values are appended to the default vector.- conf.level
Confidence level for intervals, defaults to
.95
- data
data
extracted from thelm.object
- newfit
Constructed
data.frame
containing the predictions,confidence interval, and prediction interval for thenewdata
.- ylim
ylim
for plot. Default is based on the constructed prediction interval.- pch
Plotting character for observed points.
- lty, lwd
Line types and line width for fit and intervals.
- main.cex
Font size for main title.
- main
Main title for plot
- …
Additional arguments to be passed to panel function.
Value
"trellis"
object containing the plot.
Note
The predict.lm
functions in S-Plus and R differ.
The S-Plus function can produce both confidence and prediction
intervals with a single call. The R function produces only one
of them in a single call. Therefore the default calculation of
newfit
within the function depends on the system.
See Also
Examples
# NOT RUN {
tmp <- data.frame(x=rnorm(20), y=rnorm(20))
tmp.lm <- lm(y ~ x, data=tmp)
ci.plot(tmp.lm)
# }