These functions display a generalization, due to Sall (1990) and Cook and Weisberg (1991), of added-variable plots to multiple-df terms in a linear model. When a term has just 1 df, the leverage plot is a rescaled version of the usual added-variable (partial-regression) plot.

```
leveragePlots(model, terms = ~., layout = NULL, ask,
main, ...)
```leveragePlot(model, ...)

# S3 method for lm
leveragePlot(model, term.name,
id=TRUE, col=carPalette()[1], col.lines=carPalette()[2], lwd=2,
xlab, ylab, main="Leverage Plot", grid=TRUE, ...)

# S3 method for glm
leveragePlot(model, ...)

model

model object produced by `lm`

terms

A one-sided formula that specifies a subset of the numeric regressors, factors and interactions.
One added-variable plot is drawn for each term, either a main effect or an interactions. The default
`~.`

is to plot against all terms in the model. For example, the
specification `terms = ~ . - X3`

would plot against all predictors
except for `X3`

. If this argument is a quoted name of one of the predictors, the
added-variable plot is drawn for that predictor only. The plots for main effects with interactions present violate the marginality principle and may not be easily interpreted.

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`

, a menu is provided in the R Console for the
user to select the term(s) to plot.

xlab, ylab

axis labels; if missing, labels will be supplied.

main

title for plot; if missing, a title will be supplied.

…

arguments passed down to method functions.

term.name

Quoted name of term in the model to be plotted; this argument
is omitted for `leveragePlots`

.

id

controls point identification; if `FALSE`

, no points are identified;
can be a list of named arguments to the `showLabels`

function;
`TRUE`

, the default, 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 greatest partial leverage.

col

color(s) of points

col.lines

color of the fitted line

lwd

line width; default is `2`

(see `par`

).

grid

If TRUE, the default, a light-gray background grid is put on the graph

`NULL`

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

The function intended for direct use is `leveragePlots`

.

The model can contain factors and interactions. A leverage plot can be drawn for each term in the model, including the constant.

`leveragePlot.glm`

is a dummy function, which generates an error message.

Cook, R. D. and Weisberg, S. (1991).
Added Variable Plots in Linear
Regression. In Stahel, W. and Weisberg, S. (eds.),
*Directions in Robust Statistics and Diagnostics*.
Springer, 47-60.

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.

Sall, J. (1990)
Leverage plots for general linear hypotheses.
*American Statistician* **44**, 308--315.

# NOT RUN { leveragePlots(lm(prestige~(income+education)*type, data=Duncan)) # }