plot.cancor {candisc} | R Documentation |

This function produces plots to help visualize X, Y data in canonical space.

The present implementation plots the canonical scores for the Y variables against those for the X variables on given dimensions. We treat this as a view of the data in canonical space, and so offer additional annotations to a standard scatterplot.

## S3 method for class 'cancor' plot(x, which = 1, xlim, ylim, xlab, ylab, points = TRUE, add = FALSE, col = palette()[1], ellipse = TRUE, ellipse.args = list(), smooth = FALSE, smoother.args = list(), col.smooth = palette()[3], abline = TRUE, col.lines = palette()[2], lwd = 2, labels = rownames(xy), id.method = "mahal", id.n = 0, id.cex = 1, id.col = palette()[1], ...)

`x` |
A |

`which` |
Which dimension to plot? An integer in |

`xlim, ylim` |
Limits for x and y axes |

`xlab, ylab` |
Labels for x and y axes. If not specified, these are
constructed from the |

`points` |
logical. Display the points? |

`add` |
logical. Add to an existing plot? |

`col` |
Color for points. |

`ellipse` |
logical. Draw a data ellipse for the canonical scores? |

`ellipse.args` |
A list of arguments passed to |

`smooth` |
logical. Draw a (loess) smoothed curve? |

`smoother.args` |
Arguments passed to |

`col.smooth` |
Color for the smoothed curve. |

`abline` |
logical. Draw the linear regression line for Ycan[,which] on Xcan[,which]? |

`col.lines` |
Color for the linear regression line |

`lwd` |
Line widths |

`labels` |
Point labels for point identification via the |

`id.method` |
Method used to identify individual points. See |

`id.n` |
Number of points to identify |

`id.cex, id.col` |
Character size and color for labeled points |

`...` |
Other arguments passed down to |

Canonical correlation analysis assumes that the all correlations between the X and Y variables can be expressed in terms of correlations the canonical variate pairs, (Xcan1, Ycan1), (Xcan2, Ycan2), ..., and that the relations between these pairs are indeed linear.

Data ellipses, and smoothed (loess) curves, together with the linear regression line for each canonical dimension help to assess whether there are peculiarities in the data that might threaten the validity of CCA. Point identification methods can be useful to determine influential cases.

None. Used for its side effect of producing a plot.

Michael Friendly

Mardia, K. V., Kent, J. T. and Bibby, J. M. (1979).
*Multivariate Analysis*. London: Academic Press.

`dataEllipse`

, `loessLine`

, `showLabels`

data(Rohwer, package="heplots") X <- as.matrix(Rohwer[,6:10]) # the PA tests Y <- as.matrix(Rohwer[,3:5]) # the aptitude/ability variables cc <- cancor(X, Y, set.names=c("PA", "Ability")) plot(cc) # exercise some options plot(cc, smooth=TRUE, id.n=3, ellipse.args=list(fill=TRUE)) plot(cc, which=2, smooth=TRUE) plot(cc, which=3, smooth=TRUE) # plot vectors showing structure correlations of Xcan and Ycan with their own variables plot(cc) struc <- cc$structure Xstruc <- struc$X.xscores[,1] Ystruc <- struc$Y.yscores[,1] scale <- 2 # place vectors in the margins of the plot usr <- matrix(par("usr"), nrow=2, dimnames=list(c("min", "max"), c("x", "y"))) ypos <- usr[2,2] - (1:5)/10 arrows(0, ypos, scale*Xstruc, ypos, angle=10, len=0.1, col="blue") text(scale*Xstruc, ypos, names(Xstruc), pos=2, col="blue") xpos <- usr[2,1] - ( 1 + 1:3)/10 arrows(xpos, 0, xpos, scale*Ystruc, angle=10, len=0.1, col="darkgreen") text(xpos, scale*Ystruc, names(Ystruc), pos=1, col="darkgreen")

[Package *candisc* version 0.8-5 Index]