ellipse(center, shape, radius, log="", center.pch=19, center.cex=1.5,
segments=51, add=TRUE, xlab="", ylab="",
col=palette()[2], lwd=2, fill=FALSE, fill.alpha=0.3, grid=TRUE, ...)
dataEllipse(x, y, log="", levels=c(0.5, 0.95), center.pch=19, center.cex=1.5,
plot.points=TRUE, add=!plot.points, segments=51, robust=FALSE,
xlab=deparse(substitute(x)),
ylab=deparse(substitute(y)),
col=palette()[1:2], lwd=2, fill=FALSE, fill.alpha=0.3, grid=TRUE, ...)
confidenceEllipse(model, ...)
## S3 method for class 'lm':
confidenceEllipse(model, which.coef, levels=0.95, Scheffe=FALSE,
center.pch=19, center.cex=1.5, segments=51, xlab, ylab,
col=palette()[2], lwd=2, fill=FALSE, fill.alpha=0.3, add=FALSE, ...)
## S3 method for class 'glm':
confidenceEllipse(model, which.coef, levels=0.95, Scheffe=FALSE,
center.pch=19, center.cex=1.5, segments=51, xlab, ylab,
col=palette()[2], lwd=2, fill=FALSE, fill.alpha=0.3, add=FALSE, ...)"x" if the x-axis is logged, "y" if the y-axis is
logged, and "xy"TRUE add ellipse to current plot.y is missing) a 2-column numeric matrix.x.FALSE data ellipses are added to the current scatterplot,
but points are not plotted.TRUE use the cov.trob function in the lm or glm.TRUE scale the ellipse so that its projections onto the
axes give Scheffe confidence intervals for the coefficients.2 (see par).col (default, FALSE)?0.3).plot and
line.NULL. These functions are used for their side effect: producing
plots.dataEllipse superimposes the normal-probability contours over a scatterplot
of the data.cov.trob.dataEllipse(Prestige$income, Prestige$education, levels=0.1*1:9, lty=2,
fill=TRUE, fill.alpha=0.1)
confidenceEllipse(lm(prestige~income+education, data=Prestige), Scheffe=TRUE)Run the code above in your browser using DataLab