ellipse(center, shape, radius, center.pch=19, center.cex=1.5, 
  segments=51, add=TRUE, xlab="", ylab="", 
   las=par('las'), col=palette()[2], lwd=2, lty=1, ...)
data.ellipse(x, y, levels=c(0.5, 0.9), 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)), 
  las=par('las'), col=palette()[2], pch=1, lwd=2, lty=1, ...)
confidence.ellipse(model, ...)
## S3 method for class 'lm':
confidence.ellipse(model, which.coef, levels=0.95, Scheffe=FALSE, 
  center.pch=19, center.cex=1.5, segments=51, xlab, ylab, 
  las=par('las'), col=palette()[2], lwd=2, lty=1, ...)
## S3 method for class 'glm':
confidence.ellipse(model, which.coef, levels=0.95, Scheffe=FALSE, 
  center.pch=19, center.cex=1.5, segments=51, xlab, ylab, 
  las=par('las'), col=palette()[2], lwd=2, lty=1, ...)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 MASS package
    to calculate the center and covariance matrix for the data ellipse.lm or glm.TRUE scale the ellipse so that its projections onto the
    axes give Scheffe confidence intervals for the coefficients.0, ticks labels are drawn parallel to the
    axis; set to 1 for horizontal labels (see par).1 
    (a circle, see par).2 (see par).1, a solid line (see par).plot and
    line.NULL. These functions are used for their side effect: producing
  plots.data.ellipse superimposes the normal-probability contours over a scatterplot
  of the data.cov.trob.data.ellipse(Prestige$income, Prestige$education, levels=0.1*1:9, lty=2)
confidence.ellipse(lm(prestige~income+education, data=Prestige), Scheffe=TRUE)Run the code above in your browser using DataLab