Ellipses                 package:car                 R Documentation

_E_l_l_i_p_s_e_s, _D_a_t_a _E_l_l_i_p_s_e_s, _a_n_d _C_o_n_f_i_d_e_n_c_e _E_l_l_i_p_s_e_s

_D_e_s_c_r_i_p_t_i_o_n:

     These functions draw ellipses, including data ellipses, and
     confidence ellipses for linear and generalized linear models.

_U_s_a_g_e:

     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, ...)

_A_r_g_u_m_e_n_t_s:

  center: 2-element vector with coordinates of center of ellipse.

   shape: 2 * 2 shape (or covariance) matrix.

  radius: radius of circle generating the ellipse.

center.pch: character for plotting ellipse center.

center.cex: relative size of character for plotting ellipse center.

segments: number of line-segments used to draw ellipse.

     add: if 'TRUE' add ellipse to current plot.

    xlab: label for horizontal axis.

    ylab: label for vertical axis.

       x: a numeric vector, or (if 'y' is missing) a 2-column numeric
          matrix.

       y: a numeric vector, of the same length as 'x'.

plot.points: if 'FALSE' data ellipses are added to the current
          scatterplot, but points are not plotted.

  levels: draw elliptical contours at these (normal) probability or
          confidence levels.

  robust: if 'TRUE' use the 'cov.trob' function in the 'MASS' package
          to calculate the center and covariance matrix for the data
          ellipse.

   model: a model object produced by 'lm' or 'glm'.

which.coef: 2-element vector giving indices of coefficients to plot; 
          if missing, the first two coefficients (disregarding the
          regression constant) will be selected.

 Scheffe: if 'TRUE' scale the ellipse so that its projections onto the
          axes give Scheffe confidence intervals for the coefficients.

     las: if '0', ticks labels are drawn parallel to the axis; set to
          '1' for horizontal labels (see 'par').

     col: color for points and lines; the default is the _second_ entry
          in the current color palette (see 'palette' and 'par').

     pch: plotting character for points; default is '1'  (a circle, see
          'par').

     lwd: line width; default is '2' (see 'par').

     lty: line type; default is '1', a solid line (see 'par').

     ...: other plotting parameters to be passed to 'plot' and 'line'.

_D_e_t_a_i_l_s:

     The ellipse is computed by suitably transforming a unit circle.

     'data.ellipse' superimposes the normal-probability contours over a
     scatterplot of the data.

_V_a_l_u_e:

     'NULL'. These functions are used for their side effect: producing
     plots.

_A_u_t_h_o_r(_s):

     Georges Monette Georges.Monette@mathstat.YorkU.CA  and John Fox
     jfox@mcmaster.ca

_R_e_f_e_r_e_n_c_e_s:

     Fox, J. (1997) _Applied Regression, Linear Models, and Related
     Methods._ Sage.

     Monette, G. (1990) Geometry of multiple regression and 3D
     graphics. In Fox, J. and Long, J. S. (Eds.)  _Modern Methods of
     Data Analysis._ Sage.

_S_e_e _A_l_s_o:

     'cov.trob'.

_E_x_a_m_p_l_e_s:

     data(Prestige)
     attach(Prestige)
     data.ellipse(income, education, levels=0.1*1:9, lty=2)
     confidence.ellipse(lm(prestige~income+education), Scheffe=TRUE)

