Description Usage Arguments Details Author(s) References See Also Examples

These functions produce various plots for objects of class `"anacor"`

1 2 3 4 5 6 | ```
## S3 method for class 'anacor'
plot(x, plot.type = "jointplot", plot.dim = c(1,2), col.row = "cadetblue",
col.column = "coral1", catlabels = list(label.row = TRUE, label.col = TRUE,
col.row = "cadetblue", col.column = "coral1", cex = 0.8, pos = 3),
legpos = "top", arrows = c(FALSE, FALSE), conf = 0.95, wlines = 0, asp = 1, pch = 20,
xlab, ylab, main, type, xlim, ylim, cex.axis2, ...)
``` |

`x` |
Object of class |

`plot.type` |
Type of plot to be produced (details see below): 2-D and 3-D for |

`plot.dim` |
Vector of length 2 with Dimensions to be plotted. For |

`col.row` |
Color row categories |

`col.column` |
Color column categories |

`catlabels` |
Various parameter settings for labels |

`legpos` |
Position of the legend (for |

`conf` |
Ellipsoid confidence level for |

`arrows` |
Whether arrows from the origin to the row scores (first element) or column scores (second element) should be drawn. |

`wlines` |
For |

`asp` |
Aspect ratio. |

`pch` |
Symbol for plotting points. |

`xlab` |
Label x-axis. |

`ylab` |
Label y-axis. |

`xlim` |
Scale x-axis. |

`ylim` |
Scale y-axis. |

`main` |
Plot title. |

`type` |
Whether points, lines or both should be plotted; for |

`cex.axis2` |
For |

`...` |
Additional graphical parameters. |

The following plot types are provided: `"jointplot"`

plots row and column scores into the same device, `"rowplot"`

and `"colplot"`

plot the row scores and column scores, respectively, in separate devices. For these types of plots 3-dimensional versions are provided. The graph plot is an unlabeled version of the joint plot where the points are connected by lines. Options are provided (`wlines`

) to steer the line thickness indicating the connection strength.

The regression plot (`"regplot"`

) provides two plots.
First, the unscaled solution is plotted. A frequency grid for the row
categories (x-axis) and column categories (y-axis) is produced.
The regression line is based on the category weighted means of the relative frequencies:
the blue line on the column-wise means on the x-axis and the column category on the y-axis,
the red line is based on the row categories on the x-axis and the row-wise means on the y-axis.
In a second device the scaled solution is plotted. The frequency grid is determined by
the row scores (x-axis) and the column scores(y-axis).
Now, instead of the row/column categories, the column scores (black line y-axis)
and the row scores (red line x-axis) are used.

The transformation plot (`"transplot"`

) plots the row/column categories against the row/column scores. The Benzecri plot (`"benzplot"`

) plots the observed distances against the fitted distances. It is assumed that the CA result is Benzecri scaled. The ordination diagram (`"orddiag"`

) for CCA produces a joint plot and includes the column and row covariates based on intraset correlations.

Jan de Leeuw, Patrick Mair

De Leeuw, J. and Mair, P. (2009). Simple and Canonical Correspondence Analysis Using the R Package anacor. Journal of Statistical Software, 31(5), 1-18. http://www.jstatsoft.org/v31/i05/

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | ```
## symmetric map
data(tocher)
res <- anacor(tocher)
plot(res, conf = NULL, main = "Symmetric Map")
## simple CA on Tocher data, asymmetric coordinates
res <- anacor(tocher, scaling = c("standard", "Benzecri"))
res
## Regression plots using Glass data
data(glass)
res <- anacor(glass)
plot(res, plot.type = "regplot", xlab = "fathers occupation", ylab = "sons occupation")
## Benzecri Plots for bitterling data
data(bitterling)
res1 <- anacor(bitterling, ndim = 2, scaling = c("Benzecri", "Benzecri"))
res2 <- anacor(bitterling, ndim = 5, scaling = c("Benzecri", "Benzecri"))
res2
plot(res1, plot.type = "benzplot", main = "Benzecri Distances (2D)")
plot(res2, plot.type = "benzplot", main = "Benzecri Distances (5D)")
## Column score plot,transformation plot, and ordination diagram for canonical CA
data(maxwell)
res <- anacor(maxwell$table, row.covariates = maxwell$row.covariates,
scaling = c("Goodman", "Goodman"))
res
plot(res, plot.type = "colplot", xlim = c(-1.5,1), conf = NULL)
plot(res, plot.type = "transplot", legpos = "topright")
plot(res, plot.type = "orddiag")
``` |

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