# residualPlots

##### Residual Plots and Curvature Tests for Linear Model Fits

Plots the residuals versus each term in a mean function and versus fitted values. Also computes a curvature test for each of the plots by adding a quadratic term and testing the quadratic to be zero. This is Tukey's test for nonadditivity when plotting against fitted values.

- Keywords
- regression, hplot

##### Usage

`### This is a generic function with only one required argument:`residualPlots (model, ...)

# S3 method for default
residualPlots(model, terms = ~., layout = NULL, ask,
main = "", fitted = TRUE, AsIs=TRUE, plot = TRUE,
tests = TRUE, groups, ...)

# S3 method for lm
residualPlots(model, ...)

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

### residualPlots calls residualPlot, so these arguments can be
### used with either function

residualPlot(model, ...)

# S3 method for default
residualPlot(model, variable = "fitted", type = "pearson",
groups,
plot = TRUE,
linear = TRUE,
quadratic = if(missing(groups)) TRUE else FALSE,
smoother=NULL, smoother.args=list(),
col.smooth=palette()[3],
labels,
id.method = "r",
id.n = if(id.method[1]=="identify") Inf else 0,
id.cex=1, id.col=palette()[1], id.location="lr",
col = palette()[1], col.quad = palette()[2],
pch=1,
xlab, ylab, lwd = 1, lty = 1,
grid=TRUE, key=!missing(groups), ...)
# S3 method for lm
residualPlot(model, ...)
# S3 method for glm
residualPlot(model, variable = "fitted", type = "pearson",
plot = TRUE, quadratic = FALSE,
smoother = loessLine, smoother.args=list(k=3), ...)

##### Arguments

- model
A regression object.

- terms
A one-sided formula that specifies a subset of the predictors. One residual plot is drawn for each specified. The default

`~ .`

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

would plot against all predictors except for`X3`

. To get a plot against fitted values only, use the arguments`terms = ~ 1`

, Interactions are skipped. For polynomial terms, the plot is against the first-order variable (which may be centered and scaled depending on how the`poly`

function is used). Plots against factors are boxplots. Plots against other matrix terms, like splines, use the result of`predict(model), type="terms")[, variable])`

as the horizontal axis; if the`predict`

method doesn't permit this type, then matrix terms are skipped.A grouping variable can also be specified in the terms, so, for example

`terms= ~ .|type`

would use the factor`type`

to set a different color and symbol for each level of`type`

. Any fits in the plots will also be done separately for each level of group.- 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`

, don't ask.- main
Main title for the graphs. The default is

`main=""`

for no title.- fitted
If

`TRUE`

, the default, include the plot against fitted values.- AsIs
If

`FALSE`

, terms that use the “as-is” function`I`

are skipped; if`TRUE`

, the default, they are included.- plot
If

`TRUE`

, draw the plot(s).- tests
If

`TRUE`

, display the curvature tests. With glm's, the argument`start`

is ignored in computing the curvature tests.- ...
Additional arguments passed to

`residualPlot`

and then to`plot`

.- variable
Quoted variable name for the horizontal axis, or

`"fitted"`

to plot versus fitted values.- type
Type of residuals to be used. Pearson residuals are appropriate for

`lm`

objects since these are equivalent to ordinary residuals with ols and correctly weighted residuals with wls. Any quoted string that is an appropriate value of the`type`

argument to`residuals.lm`

or`"rstudent"`

or`"rstandard"`

for Studentized or standardized residuals.- groups
A list of group indicators. Points in different groups will be plotted with different colors and symbols. If missing, no grouping. In

`residualPlots`

, the grouping variable can also be set in the`terms`

argument, as described above. The default is no grouping.- linear
If

`TRUE`

, adds a horizontal line at zero if no groups. With groups, display the within level of groups ols regression of the residuals as response and the horizontal axis as the regressor.- quadratic
if

`TRUE`

, fits the quadratic regression of the vertical axis on the horizontal axis and displays a lack of fit test. Default is`TRUE`

for`lm`

and`FALSE`

for`glm`

or if`groups`

not missing.- smoother
the name of the smoother to use, selected from the choices described at

`ScatterplotSmoothers`

For`lm`

objects the default is`NULL`

. For`glm`

object the default is`loessLine`

.- smoother.args
arguments passed to the smoother. See

`ScatterplotSmoothers`

. For generalized linear models the number of elements in the spline basis is set to`k=3`

; this is done to allow fitting for predictors with just a few support points. If you have many support points you may wish to set`k`

to a higher number, or`k=-1`

for the default used by`gam`

.- col.smooth
color for the smoother if groups missing, and ignored if groups is set.

- id.method,labels,id.n,id.cex,id.col,id.location
Arguments for the labelling of points. The default is

`id.n=0`

for labeling no points. See`showLabels`

for details of these arguments.- col
default color for points. If groups is set, col can abe a list at least as long as the number of levels for groups giving the colors for each groups.

- col.quad
default color for quadratic fit if groups is missing. Ignored if groups are used.

- pch
plotting character. The default is pch=1. If groups are used, pch can be set to a vector at least as long as the number of groups.

- xlab
X-axis label. If not specified, a useful label is constructed by the function.

- ylab
Y-axis label. If not specified, a useful label is constructed by the function.

- lwd
line width for lines.

- lty
line type for quadratic.

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

- key
Should a key be added to the plot? Default is

`!is.null(groups)`

.

##### Details

`residualPlots`

draws one or more residuals plots depending on the
value of the `terms`

and `fitted`

arguments. If `terms = ~ .`

,
the default, then a plot is produced of residuals versus each first-order
term in the formula used to create the model. Interaction terms, spline terms,
and polynomial terms of more than one predictor are
skipped. In addition terms that use the “as-is” function, e.g., `I(X^2)`

,
will also be skipped unless you set the argument `AsIs=TRUE`

. A plot of
residuals versus fitted values is also included unless `fitted=FALSE`

.

In addition to plots, a table of curvature tests is displayed. For plots
against a term in the model formula, say `X1`

, the test displayed is
the t-test for for `I(X^2)`

in the fit of `update, model, ~. + I(X^2))`

.
Econometricians call this a specification test. For factors, the displayed
plot is a boxplot, no curvature test is computed, and grouping is ignored.
For fitted values, the test is Tukey's one-degree-of-freedom test for
nonadditivity. You can suppress the tests with the argument `tests=FALSE`

.
If grouping is used curvature tests are not displayed.

`residualPlot`

, which is called by `residualPlots`

,
should be viewed as an internal function, and is included here to display its
arguments, which can be used with `residualPlots`

as well. The
`residualPlot`

function returns the curvature test as an invisible result.

`residCurvTest`

computes the curvature test only. For any factors a
boxplot will be drawn. For any polynomials, plots are against the linear term.
Other non-standard predictors like B-splines are skipped.

##### Value

For `lm`

objects,
returns a data.frame with one row for each plot drawn, one column for
the curvature test statistic, and a second column for the corresponding
p-value. This function is used primarily for its side effect of drawing
residual plots.

##### References

Fox, J. and Weisberg, S. (2011)
*An R Companion to Applied Regression*, Second Edition. Sage.

Weisberg, S. (2014) *Applied
Linear Regression*, Fourth Edition, Wiley, Chapter 8

##### See Also

See Also `lm`

, `identify`

,
`showLabels`

##### Examples

```
# NOT RUN {
m1 <- lm(prestige ~ income, data=Prestige)
residualPlots(m1)
residualPlots(m1, terms= ~ 1 | type) # plot vs. yhat grouping by type
# }
```

*Documentation reproduced from package car, version 2.1-6, License: GPL (>= 2)*