Dots plot displaying the deviations of factor levels from the mean showing the residuals as reference distribution.

1 2 3 |

`obj` |
Object of class |

`stacked` |
logical. If |

`base` |
logical. By default a base line is displayed for each factor.
If |

`axes` |
logical. By default a scaled axes is drawn for each factor.
If |

`faclab` |
logical. By default factor effect names and ‘Residuals’ are used to label each dot plot. No axis is labelled otherwise. |

`labels` |
logical. By default, dots are used to the display.
If |

`cex` |
numeric. Expansion factor of the character used for labelling the factor levels. |

`cex.lab` |
numeric. Expansion factor of the character used for labelling each factor. |

`...` |
additional parameters passed to the |

Dots plot are displayed for the scaled deviations of factor levels from
the grand mean and the distribution of the residuals is shown at the bottom
of the plot for graphical comparison. The scaled factor for the factor
deviations is *sqrt(n / k)*, where *k* and *n*
are the factor and residuals degrees of freedom reported by `anova(obj)`

.
If `labels=TRUE`

then the factor levels are used for as points
instead of dots. This option is useful to post labelling the dot plots.
See `dots`

function.
The Anova plot is built in a *(0,1)x(0,1)* plot
area. The area plot is divided to accommodate each of the factors and
the residual at the bottom of the plotting area. The function returns a
list with the coordinates of all the dots displayed.

The function is called for graphical display of factor levels mean and residuals as reference distribution. An invisible list with the actual (x,y) coordinates used for each of the factors and residuals.

The function identifies as an interaction factor any factor with the colon character ":" in its name. Factors like "I(A:B)" will give you problems.

The anova plot presented here is thought for graphical comparison of factor effects in one-layer balanced designed experiments. The function is not prepared for general situations. However, representation of some simple split-plot experiments is possible.

Ernesto Barrios

Box G. E. P. (2000). *Box on Quality*.
Edited by G. C. Tiao et al. New York: Wiley.

Box G. E. P, Hunter, J. S. and Hunter, W. C. (2005).
*Statistics for Experimenters II*. New York: Wiley.

1 2 3 4 5 6 7 8 9 | ```
library(BHH2)
data(heads.data)
heads.data$periods <- factor(heads.data$periods)
heads.data$heads <- factor(heads.data$heads)
heads.aov <- aov(resp~periods+heads,data=heads.data)
anovaPlot(heads.aov)
anovaPlot(heads.aov,labels=TRUE,faclab=TRUE)
``` |

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