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car

ceresPlots

Ceres Plots

Description

These functions draw Ceres plots for linear and generalized linear models.

Usage

ceresPlots(model, ...)

## Default S3 method:
ceresPlots(model, terms = ~., layout = NULL, ask, main,
    ...)

ceresPlot(model, ...)

## S3 method for class 'lm'
ceresPlot(model, variable, id=FALSE,
  line=TRUE,  smooth=TRUE, col=carPalette()[1], col.lines=carPalette()[-1],
  xlab, ylab, pch=1, lwd=2,  grid=TRUE, ...)

## S3 method for class 'glm'
ceresPlot(model, ...)

Arguments

`model`	model object produced by `lm` or `glm`.
`terms`	A one-sided formula that specifies a subset of the regressors. One component-plus-residual plot is drawn for each term. The default `~.` is to plot against all numeric predictors. For example, the specification `terms = ~ . - X3` would plot against all predictors except for `X3`. Factors and nonstandard predictors such as B-splines are skipped. If this argument is a quoted name of one of the regressors, the component-plus-residual plot is drawn for that predictor only.
`layout`	If set to a value like `c(1, 1)` or `c(4, 3)`, the layout of the graph will have this many rows and columns. If not set, the program will select an appropriate layout. If the number of graphs exceed nine, you must select the layout yourself, or you will get a maximum of nine per page. If `layout=NA`, the function does not set the layout and the user can use the `par` function to control the layout, for example to have plots from two models in the same graphics window.
`ask`	If `TRUE`, ask the user before drawing the next plot; if `FALSE`, the default, don't ask. This is relevant only if not all the graphs can be drawn in one window.
`main`	Overall title for any array of cerers plots; if missing a default is provided.
`...`	`ceresPlots` passes these arguments to `ceresPlot`. `ceresPlot` passes them to `plot`.
`variable`	A quoted string giving the name of a variable for the horizontal axis
`id`	controls point identification; if `FALSE` (the default), no points are identified; can be a list of named arguments to the `showLabels` function; `TRUE` is equivalent to `list(method=list(abs(residuals(model, type="pearson")), "x"), n=2, cex=1, col=carPalette()[1], location="lr")`, which identifies the 2 points with the largest residuals and the 2 points with the most extreme horizontal (X) values.
`line`	`TRUE` to plot least-squares line.
`smooth`	specifies the smoother to be used along with its arguments; if `FALSE`, no smoother is shown; can be a list giving the smoother function and its named arguments; `TRUE`, the default, is equivalent to `list(smoother=loessLine)`. See `ScatterplotSmoothers` for the smoothers supplied by the car package and their arguments. Ceres plots do not support variance smooths.
`col`	color for points; the default is the first entry in the current car palette (see `carPalette` and `par`).
`col.lines`	a list of at least two colors. The first color is used for the ls line and the second color is used for the fitted lowess line. To use the same color for both, use, for example, `col.lines=c("red", "red")`
`xlab,ylab`	labels for the x and y axes, respectively. If not set appropriate labels are created by the function.
`pch`	plotting character for points; default is `1` (a circle, see `par`).
`lwd`	line width; default is `2` (see `par`).
`grid`	If TRUE, the default, a light-gray background grid is put on the graph

Details

Ceres plots are a generalization of component+residual (partial residual) plots that are less prone to leakage of nonlinearity among the predictors.

The function intended for direct use is ceresPlots.

The model cannot contain interactions, but can contain factors. Factors may be present in the model, but Ceres plots cannot be drawn for them.

Value

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

Author(s)

John Fox jfox@mcmaster.ca

References

Cook, R. D. and Weisberg, S. (1999) Applied Regression, Including Computing and Graphics. Wiley.

Fox, J. (2016) Applied Regression Analysis and Generalized Linear Models, Third Edition. Sage.

Fox, J. and Weisberg, S. (2019) An R Companion to Applied Regression, Third Edition, Sage.

Examples

ceresPlots(lm(prestige~income+education+type, data=Prestige), terms= ~ . - type)

car

Companion to Applied Regression

v3.0-10

GPL (>= 2)

Authors

John Fox [aut, cre], Sanford Weisberg [aut], Brad Price [aut], Daniel Adler [ctb], Douglas Bates [ctb], Gabriel Baud-Bovy [ctb], Ben Bolker [ctb], Steve Ellison [ctb], David Firth [ctb], Michael Friendly [ctb], Gregor Gorjanc [ctb], Spencer Graves [ctb], Richard Heiberger [ctb], Pavel Krivitsky [ctb], Rafael Laboissiere [ctb], Martin Maechler [ctb], Georges Monette [ctb], Duncan Murdoch [ctb], Henric Nilsson [ctb], Derek Ogle [ctb], Brian Ripley [ctb], William Venables [ctb], Steve Walker [ctb], David Winsemius [ctb], Achim Zeileis [ctb], R-Core [ctb]

Initial release

2020-09-23