plotmo {plotmo}R Documentation

Plot a model's response over a range of predictor values


Plot a model's response when varying one or two predictors while holding the other predictors constant. A poor man's partial dependence plot.

Please see the plotmo vignette.


plotmo(object=stop("no 'object' argument"),
    type=NULL, nresponse=NA,
    pt.col=0, jitter=.5, smooth.col=0, level=0,
    func=NULL, inverse.func=NULL, nrug=0, grid.col=0,
    degree1=TRUE, all1=FALSE, degree2=TRUE, all2=FALSE,
    do.par=TRUE, clip=TRUE, ylim=NULL, caption=NULL, trace=0,
    grid.func=median, grid.levels=NULL, extend=0,
    ngrid1=50, ngrid2=20, ndiscrete=5, npoints=3000,
    center=FALSE, xflip=FALSE, yflip=FALSE, swapxy=FALSE, int.only.ok=TRUE,



The model object.


Type parameter passed to predict. For allowed values see the predict method for your object (such as By default, plotmo 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 predict.


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


The color of response points (or response sites in degree2 plots). This refers to the response y in the data used to build the model. Note that the displayed points are jittered by default (see the jitter argument).
Default is 0, display no response points.
This can be a vector, like all such arguments, for example pt.col = as.numeric(survived)+2.
You can modify the plotted points with pt.pch, pt.cex, etc. (these get passed via plotmo's “...” argument). To label the points, set pt.pch to a character vector.


Applies only if pt.col is specified.
The default is jitter=.5, automatically apply some jitter to the points. Points are jittered horizontally and vertically.
Use jitter=0 to disable this automatic jittering. Otherwise something like jitter=1, but the optimum value is data dependent.


Color of smooth line through the response points. (The points themselves will not be plotted unless pt.col is specified.) Default is 0, no smooth line.

    mod <- lm(Volume~Height, data=trees)
    plotmo(mod, pt.color=1, smooth.col=2)

You can adjust the amount of smoothing with smooth.f. This gets passed as f to lowess. The default is .5. Lower values make the line more wiggly.


Draw estimated confidence or prediction interval bands at the given level, if the predict method for the model supports them.
Default is 0, bands not plotted. Else a fraction, for example level=.95. See “Prediction intervals” in the plotmo vignette. Example:

    mod <- lm(log(Volume)~log(Girth), data=trees)
    plotmo(mod, level=.95)

You can modify the color of the bands with level.shade and level.shade2.


Superimpose func(x) on the plot. Example:

    mod <- lm(Volume~Girth, data=trees)
    estimated.volume <- function(x) .17 * x$Girth^2
    plotmo(mod, pt.col=2, func=estimated.volume)

The func is called for each plot with a single argument which is a data frame with columns in the same order as the predictors in the formula or x used to build the model. Use trace=2 to see the column names and first few rows of this dataframe.


A function applied to the response before plotting. Useful to transform a transformed response back to the original scale. Example:

    mod <- lm(log(Volume)~., data=trees)
    plotmo(mod, inverse.func=exp)    # exp() is inverse of log()


Number of points in the rug rug along the bottom of the plot (a sample of nrug points is plotted). Default is 0, no rug. Use nrug=TRUE or -1 for all points. The rug is jittered to match the jittered points (see the jitter argument).
The special value nrug="density" means plot the density of the points along the bottom. You can modify the density plot with density.adjust (default is .5), density.col, density.lty, etc.


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".


Degree2 plot type. One of "persp" (default), "image", or "contour". You can pass arguments to these functions if necessary by using persp., image., or contour. as a prefix. Examples:

    plotmo(mod, persp.ticktype="detailed", persp.nticks=2)
    plotmo(mod, type2="image")
    plotmo(mod, type2="image", image.col=heat.colors(12))
    plotmo(mod, type2="contour", contour.col=2, contour.labcex=.4)

An index vector specifying which subset of degree1 (main effect) plots to include (after selecting the relevant predictors as described in “Which variables are plotted?” in the plotmo vignette).
Default is TRUE, meaning all (the TRUE gets recycled). Use degree=FALSE or 0 for no degree1 plots.
Can also be a character vector specifying which variables to plot e.g. degree1=c("wind", "vis"). Variable are matched with grep. Thus "wind" will match all variables with "wind" anywhere in their name. Use "^wind$" to match only the variable named "wind".
Note that an integer degree1 indexes plots on the page, not columns of x. Probably the easiest way to use this argument (and degree2) is to first use the default (and possibly all1=TRUE) to plot all figures. This shows how the figures are numbered. Then replot using degree1 to select the figures you want, for example degree1=c(1,3,4).


Default is FALSE. Use TRUE to plot all predictors, not just those usually selected by plotmo.
The all1 argument increases the number of plots; the degree1 argument reduces the number of plots.


An index vector specifying which subset of degree2 (interaction) plots to include.
Default is TRUE meaning all (after selecting the relevant interaction terms as described in “Which variables are plotted?” in the plotmo vignette).
Can also be a character vector specifying which variables to plot (grep is used for matching). For example, degree2="vis" selects degree2 plots for the vis variable.


Default is FALSE. Use TRUE to plot all pairs of predictors, not just those usually selected by plotmo.


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

do.par=NULL. 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 (default). 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 plotmo exits, so you can add something to the plot.


The default is clip=TRUE, meaning ignore very outlying predictions when determining the automatic ylim. This keeps ylim fairly compact while still covering all or nearly all the data, even if there are a few crazy predicted values. See “The ylim and clip arguments” in the plotmo vignette.
Use clip=FALSE for no clipping.


Three possibilities:
ylim=NULL (default). Automatically determine a ylim to use across all graphs.
ylim=NA. Each graph has its own ylim.
ylim=c(ymin,ymax). Use the specified limits across all graphs.


Overall caption. By default create the caption automatically. Use caption="" for no caption. (Use main to set the title of individual plots, can be a vector.)


Default is 0.
trace=1 (or TRUE) for a summary trace (shows how predict is invoked for the current object).
trace=2 for detailed tracing.
trace=-1 inhibits the messages usually issued by plotmo, like the “plotmo grid:” and “nothing to plot” messages. Error and warning messages will be printed as usual.


Function applied to columns of the x matrix to pin the values of variables not on the axes. Default is median. This argument is not related to the grid.col argument. Examples:

    plotmo(mod, grid.func=mean)
    grid.func <- function(x, ...) quantile(x)[2] # 25% quantile
    plotmo(mod, grid.func=grid.func)

This argument is ignored for factors. The first level of factors is used. That can be changed with grid.levels, see below.


Default is NULL. Else a list of variables and their fixed value to be used when the variable is not on the axis. Supersedes grid.func for variables in the list. Names and values can be abbreviated, partial matching is used. Example:

    plotmo(mod, grid.levels=list(sex="m", age=21))


Amount to extend the horizontal axis in each plot. The default is 0, do not extend (i.e. use the range of the variable in the training data). Else something like extend=.5, which will extend both the lower and upper xlim of each plot by 50%.
This argument is useful if you want to see how the model performs on data that is beyond the training data; for example, you want to see how a time-series model performs on future data.
This argument is currently implemented only for degree1 plots. Factors and discrete variables (see the ndiscrete argument) are not extended.


Number of equally spaced x values in each degree1 plot. Default is 50.


Grid size for degree2 plots (ngrid2 x ngrid2 points are plotted). Default is 20.
The default will sometimes be too small for contour and image plots.
With large ngrid2 values, persp plots look better with persp.border=NA.


Number of response points to be plotted (a sample of npoints points is plotted). Applies only if pt.col is specified.
The default is 3000 (not all, to avoid overplotting on large models). Use npoints=TRUE or -1 for all points.


Default 5 (a somewhat arbitrary value). Variables with no more than ndiscrete unique values are plotted as quantized in plots (a staircase rather than a curve). Factors are always considered discrete.


Plot the model even if it is an intercept-only model. Do this by plotting a single degree1 plot for the first predictor.
The default is TRUE. Use int.only.ok=FALSE to instead issue an error message for intercept-only models.


Center the plotted response. Default is FALSE.


Default FALSE. Use TRUE to flip the direction of the x axis. This argument (and yflip and swapxy) is useful when comparing to a plot from another source and you want the axes to be the same. (Note that xflip and yflip cannot be used on the persp plots, a limitation of the persp function.)


Default FALSE. Use TRUE to flip the direction of the y axis of the degree2 graphs.


Default FALSE. Use TRUE to swap the x and y axes on the degree2 graphs.


Dot arguments are passed to the predict and 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 persp.col="pink" passes col="pink" to persp(), overriding the global col setting. To send an argument to predict whose name may alias with plotmo's arguments, use predict. as a prefix. Example:

    plotmo(mod, s=1)           # error:  arg matches multiple formal args
    plotmo(mod, predict.s=1)   # ok now: s=1 will be passed to predict()

The prefixes recognized by plotmo are:

predict. passed to the predict method for the model
degree1. modifies degree1 plots e.g. degree1.col=3, degree1.lwd=2
persp. arguments passed to persp
contour. arguments passed to contour
image. arguments passed to image
pt. see the pt.col argument (arguments passed to points and text)
smooth. see the smooth.col argument (arguments passed to lines and lowess)
level. see the level argument (level.shade, level.shade2, and arguments for polygon)
func. see the func argument (arguments passed to lines)
rug. see the nrug argument (rug.jitter, and arguments passed to rug)
density. see the nrug (sic) argument (density.adjust, and arguments passed to lines)
grid. see the grid.col argument (arguments passed to grid)
caption. see the caption argument (arguments passed to mtext)
par. arguments passed to par (only necessary if a par argument name clashes with a plotmo 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. For example, the old argument col.response is no longer in plotmo's formal argument list, but is still accepted and treated like the new argument pt.col.


In general this function won't work on models that don't save the call and data with the model in a standard way. For further discussion please see “Accessing the model data” in the plotmo vignette. Package authors may want to look at Guidelines for S3 Regression Models.

By default, plotmo tries to use sensible model-dependent defaults when calling predict. Use trace=1 to see the arguments passed to predict. You can change the defaults by using plotmo's type argument, and by using dot arguments prefixed with predict. (see the description of “...” above).

See Also

Please see the plotmo vignette.


if (require(rpart)) {
    rpart.model <- rpart(Kyphosis~., data=kyphosis)
    plotmo(rpart.model, type="prob", nresponse="present")
if (require(earth)) {
    earth.model <- earth(O3 ~ ., data=ozone1, degree=2)

[Package plotmo version 3.2.1 Index]