# plotres: Plot the residuals of a regression model In plotmo: Plot a Model's Response and Residuals

## Description

Plot the residuals of a regression model.

Please see the plotres vignette (also available here).

## Usage

 ```1 2 3 4 5 6 7 8 9``` ```plotres(object = stop("no 'object' argument"), which = 1:4, info = FALSE, versus = 1, standardize = FALSE, delever = FALSE, level = 0, id.n = 3, labels.id = NULL, smooth.col = 2, grid.col = 0, jitter = 0, do.par = NULL, caption = NULL, trace = 0, npoints = 3000, center = TRUE, type = NULL, nresponse = NA, object.name = quote.deparse(substitute(object)), ...) ```

## Arguments

 `object` The model object. `which` Which plots do draw. Default is `1:4`. `1` Model plot. What gets plotted here depends on the model class. For example, for `earth` models this is a model selection plot. Nothing will be displayed for some models. For details, please see the plotres vignette. `2` Cumulative distribution of abs residuals `3` Residuals vs fitted `4` QQ plot `5` Abs residuals vs fitted `6` Sqrt abs residuals vs fitted `7` Abs residuals vs log fitted `8` Cube root of the squared residuals vs log fitted `9` Log abs residuals vs log fitted `info` Default is `FALSE`. Use `TRUE` to print extra information as follows: i) Display the distribution of the residuals along the bottom of the plot. ii) Display the training R-Squared. iii) Display the Spearman Rank Correlation of the absolute residuals with the fitted values. Actually, correlation is measured against the absolute values of whatever is on the horizontal axis — by default this is the fitted response, but may be something else if the `versus` argument is used. iv) In the Cumulative Distribution plot (`which=2`), display additional information on the quantiles. v) Only for `which=5` or `9`. Regress the absolute residuals against the fitted values and display the regression slope. Robust linear regression is used via `rlm` in the MASS package. vi) Add various annotations to the other plots. `versus` What do we plot the residuals against? One of: `1` Default. Plot the residuals versus the fitted values (or the log values when `which=7` to `9`). `2` Residuals versus observation number, after observations have been sorted on the fitted value. Same as `versus=1`, except that the residuals are spaced uniformly along the horizontal axis. `3` Residuals versus the response. `4` Residuals versus the hat leverages. `"b:"` Residuals versus the basis functions. Currently only supported for `earth`, `mda::mars`, and `gam::gam` models. A optional `regex` can follow the `"b:"` to specify a subset of the terms, e.g. `versus="b:wind"` will plot terms with `"wind"` in their name. Else a character vector specifying which predictors to plot against. Example 1: `versus=""` plots against all predictors (since the regex `versus=""` matches anything). Example 2: `versus=c("wind", "vis")` plots predictors with `wind` or `vis` in their name. Example 3: `versus=c("wind|vis")` equivalent to the above. Note: These are `regex`s. Thus `versus="wind"` will match all variables that have `"wind"` in their names. Use `"^wind\$"` to match only the variable named `"wind"`. `standardize` Default is `FALSE`. Use `TRUE` to standardize the residuals. Only supported for some models, an error message will be issued otherwise. Each residual is divided by by `se_i * sqrt(1 - h_ii)`, where `se_i` is the standard error of prediction and `h_ii` is the leverage (the diagonal entry of the hat matrix). When the variance model holds, the standardized residuals are homoscedastic with unity variance. The leverages are obtained using `hatvalues`. (For `earth` models the leverages are for the linear regression of the response on the basis matrix `bx`.) A standardized residual with a leverage of 1 is plotted as a star on the axis. This argument applies to all plots where the residuals are used (including the cumulative distribution and QQ plots, and to annotations displayed by the `info` argument). `delever` Default is `FALSE`. Use `TRUE` to “de-lever” the residuals. Only supported for some models, an error message will be issued otherwise. Each residual is divided by `sqrt(1 - h_ii)`. See the `standardize` argument for details. `level` Draw estimated confidence or prediction interval bands at the given `level`, if the model supports them. Default is `0`, bands not plotted. Else a fraction, for example `level=0.90`. Example: ``` mod <- lm(log(Volume)~log(Girth), data=trees) plotres(mod, level=.90)``` You can modify the color of the bands with `level.shade` and `level.shade2`. See also “Prediction intervals” in the plotmo vignette (but note that `plotmo` needs prediction intervals on new data, whereas `plotres` requires only that the model supports prediction intervals on the training data). `id.n` The largest `id.n` residuals will be labeled in the plot. Default is `3`. Special values `TRUE` and `-1` or mean all. If `id.n` is negative (but not `-1`) the `id.n` most positive and most negative residuals will be labeled in the plot. A current implementation restriction is that `id.n` is ignored when there are more than ten thousand cases. `labels.id` Residual labels. Only used if `id.n > 0`. Default is the case names, or the case numbers if the cases are unnamed. `smooth.col` Color of the smooth line through the residual points. Default is `2`, red. Use `smooth.col=0` for no smooth line. You can adjust the amount of smoothing with `smooth.f`. This gets passed as `f` to `lowess`. The default is `2/3`. Lower values make the line more wiggly. `grid.col` Default is `0`, no grid. Else add a background `grid` of the specified color to the degree1 plots. The special value `grid.col=TRUE` is treated as `"lightgray"`.
`jitter`

Default is `0`, no jitter. Passed as `factor` to `jitter` to jitter the plotted points horizontally and vertically. Useful for discrete variables and responses, where the residual points tend to be overlaid.

`do.par`

One of `NULL`, `FALSE`, `TRUE`, or `2`, as follows:

`do.par=NULL` (default). Same as `do.par=FALSE` if the number of plots is one; else the same as `TRUE`.

`do.par=FALSE`. Use the current `par` settings. You can pass additional graphics parameters in the “`...`” argument.

`do.par=TRUE`. Start a new page and call `par` as appropriate to display multiple plots on the same page. This automatically sets parameters like `mfrow` and `mar`. You can pass additional graphics parameters in the “`...`” argument.

`do.par=2`. Like `do.par=TRUE` but don't restore the `par` settings to their original state when `plotres` exits, so you can add something to the plot.

`caption`

Overall caption. By default create the caption automatically. Use `caption=""` for no caption. (Use `main` to set the title of an individual plot.)

`trace`

Default is `0`.
`trace=1` (or `TRUE`) for a summary trace (shows how `predict` and friends are invoked for the model).
`trace=2` for detailed tracing.

`npoints`

Number of points to be plotted. A sample of `npoints` is taken; the sample includes the biggest twenty or so residuals.
The default is 3000 (not all, to avoid overplotting on large models). Use `npoints=TRUE` or `-1` for all points.

`center`

Default is TRUE, meaning center the horizontal axis in the residuals plot, so asymmetry in the residual distribution is more obvious.

`type`

Type parameter passed first to `residuals` and if that fails to `predict`. For allowed values see the `residuals` and `predict` methods for your `object` (such as `residuals.rpart` or `predict.earth`). By default, `plotres` tries to automatically select a suitable value for the model in question (usually `"response"`), but this will not always be correct. Use `trace=1` to see the `type` argument passed to `residuals` and `predict`.

`nresponse`

Which column to use when `residuals` or `predict` returns multiple columns. This can be a column index or column name (which may be abbreviated, partial matching is used).

`object.name`

The name of the `object` for error and trace messages. Used internally by `plot.earth`.

`...`

Dot arguments are passed to the plot functions. Dot argument names, whether prefixed or not, should be specified in full and not abbreviated.

“Prefixed” arguments are passed directly to the associated function. For example the prefixed argument `pt.col="pink"` passes `col="pink"` to `points()`, overriding the global `col` setting. The prefixes recognized by `plotres` are:

 `residuals.` passed to `residuals` `predict.` passed to `predict` (`predict` is called if the call to `residuals` fails) `w1.` sent to the model-dependent plot for `which=1` e.g. `w1.col=2` `pt.` modify the displayed points e.g. `pt.col=as.numeric(survived)+2` or `pt.cex=.8`. `smooth.` modify the smooth line e.g. `smooth.col=0` or `smooth.f=.5`. `level.` modify the interval bands, e.g. `level.shade="gray"` or `level.shade2="lightblue"` `legend.` modify the displayed `legend` e.g. `legend.cex=.9` `cum.` modify the Cumulative Distribution plot (arguments for `plot.stepfun`) `qq.` modify the QQ plot, e.g. `qq.pch=1` `qqline` modify the `qqline` in the QQ plot, e.g. `qqline.col=0` `label.` modify the point labels, e.g. `label.cex=.9` or `label.font=2` `cook.` modify the Cook's Distance annotations. This affects only the leverage plot (`versus=3`) for `lm` models with `standardize=TRUE`. e.g. `cook.levels=c(.5, .8, 1)` or `cook.col=2`. `caption.` modify the overall caption (see the `caption` argument) e.g. `caption.col=2`. `par.` arguments for `par` (only necessary if a `par` argument name clashes with a `plotres` argument)

The `cex` argument is relative, so specifying `cex=1` is the same as not specifying `cex`.

For backwards compatibility, some dot arguments are supported but not explicitly documented.

## Value

If the `which=1` plot was plotted, the return value of that plot (model dependent).

Else if the `which=3` plot was plotted, return `list(x,y)` where `x` and `y` are the coordinates of the points in that plot (but without jittering even if the `jitter` argument was used).

Else return `NULL`.

## Note

This function is designed primarily for displaying standard `response - fitted` residuals for models with a single continuous response, although it will work for a few other models.

In general this function won't work on models that don't save the call and data with the model in a standard way. It uses the same underlying mechanism to access the model data as `plotmo`. For further discussion please see “Accessing the model data” in the plotmo vignette (also available here). Package authors may want to look at Guidelines for S3 Regression Models (also available here).

Please see the plotres vignette (also available here).

`plot.lm`

`plot.earth`

## Examples

 ```1 2 3 4 5 6``` ```# we use lm in this example, but plotres is more useful for models # that don't have a function like plot.lm for plotting residuals lm.model <- lm(Volume~., data=trees) plotres(lm.model) ```

plotmo documentation built on May 15, 2018, 5:05 p.m.