plotmo (version 3.6.0)

plotmo: Plot a model's response over a range of predictor values (the model surface)


Plot model surfaces for a wide variety of models.

This function plots the model's response when varying one or two predictors while holding the other predictors constant (a poor man's partial-dependence plot).

It can also generate partial-dependence plots (by specifying pmethod="partdep").

Please see the plotmo vignette (also available here).


plotmo(object=stop("no 'object' argument"),
    type=NULL, nresponse=NA, pmethod="plotmo",
    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=NULL, 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 a column name if the predict method for the model returns column names. The column name may be abbreviated, partial matching is used.


Plotting method. One of:

"plotmo" (default) Classic plotmo plots i.e. the background variables are fixed at their medians (or first level for factors).

"partdep" Partial dependence plots, i.e. at each point the effect of the background variables is averaged.

"apartdep" Approximate partial dependence plots. Faster than "partdep" especially for big datasets. Like "partdep" but the background variables are averaged over a subset of ngrid1 cases (default 50), rather than all cases in the training data. The subset is created by selecting rows at equally spaced intervals from the training data after sorting the data on the response values (ties are randomly broken). The same background subset of ngrid1 cases is used for both degree1 and degree2 plots.


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 to color points by their survival class.

You can modify the plotted points with pt.pch, pt.cex, etc. (these get passed via plotmo's ``...'' argument). For example, pt.cex = weights to size points by their weight. 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 dataframe 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 ticks in the rug along the bottom of the plot

Default is 0, no rug.

Use nrug=TRUE for all the points.

Else specify the number of quantiles e.g. use nrug=10 for ticks at the 0, 10, 20, ..., 100 percentiles.

Modify the rug ticks with rug.col, rug.lwd, etc.

The special value nrug="density" means plot the density of the points along the bottom. 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=3)
    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). To plot only the third plot use degree1=3. For no degree1 plots use degree1=0.

Note that 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).

Can also be a character vector specifying which variables to plot. Examples: degree1="wind" degree1=c("wind", "vis").

Variables names 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".


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). Examples:

degree2="wind" plots all degree2 plots for the wind variable.

degree2=c("wind", "vis") plots just the wind:vis plot.


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:, calculating partdep, 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 axis of the current plot (the ``background'' variables). The default is a function which for numeric variables returns the median and for logical and factors variables returns the value occurring most often in the training data. Examples:

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

This argument is not related to the grid.col argument. This argument can be overridden for specific variables---see grid.levels 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. Also used as the number of background cases for pmethod="apartdep".


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. Variables with non-integer values are always considered non-discrete. Use ndiscrete=0 if you want to plot the response for a variable with just a few integer values as a line or a curve, rather than a staircase.


Plot the model even if it is an intercept-only model (no predictors are used in the 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:

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 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) prednames.
Use prednames.abbreviate=FALSE for full predictor names in graph axes.

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.

See Also

Please see the plotmo vignette (also available here).


if (require(rpart)) {
    rpart.model <- rpart(Kyphosis~., data=kyphosis)
    # pass type="prob" to plotmo's internal calls to predict.rpart, and
    # select the column named "present" from the matrix returned by predict.rpart
    plotmo(rpart.model, type="prob", nresponse="present")
if (require(earth)) {
    earth.model <- earth(O3 ~ ., data=ozone1, degree=2)
    # plotmo(earth.model, pmethod="partdep") # partial dependence plots
# }