plotmo(object = stop("no 'object' arg"),
type=NULL, nresponse = NA, clip = TRUE, ylim = NULL,
degree1 = TRUE, all1=FALSE, degree2 = TRUE, all2=FALSE,
grid.func = median, grid.levels = NULL,
col.response = 0, cex.response = 1, pch.response = 1,
jitter.response=0, inverse.func = NULL,
xflip=FALSE, yflip=FALSE, swapxy=FALSE,
trace = FALSE, nrug = 0,
se = 0, col.shade = "lightblue", col.se = 0, lty.se = 2,
func = NULL, col.func = "pink", lwd.func = 1, lty.func = 1,
ngrid1 = 500, lty.degree1 = 1, col.degree1 = 1,
type2 = "persp", ngrid2 = 20,
col.image = grey(0:9/10), col.persp = "lightblue",
theta = NA, phi = 30, shade = 0.5,
do.par = TRUE, caption = NULL, main = NULL,
xlab = "", ylab = "", cex = NULL, cex.lab = 1, ...)predict.
For legal values see the predict method for
your object; for example, see
predict.lm or predict returns multiple columns.
This can be a column index or column name
(which may be abbreviated, partial matching is used).
Ignored when predict returns a single column.TRUE, meaning plot only predicted values that
are in the range of the response of the original data.
Use FALSE to plot all predicted values.NULL (default) all y axes have same limits
(where ``y'' is actually ``z'' on degree2 plots).
The limits are the min and max values of the predicted response
across all plots (after applyingTRUE, meaning all degree1 plots (the TRUE gets recycled).
Use FALSE (or 0 or NA) for no degree1<FALSE.
Use TRUE to plot all predictors,
not just those usually selected by plotmo.
See ``Which variables get plotted?'' below.
The all1 argument increases the numbTRUE, meaning all degree2 plots.
Use FALSE for no degree2 plots.FALSE.
Use TRUE to plot all pairs of predictors,
not just those usually selected by plotmo.median.
(This argument is ignored for factors. The first level of
factors is used. 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 can be abbreviated, partial matching is usdegree2 plots).
Here ``response'' refers to the response in the original data
used to build the model.
Default is 0, don't plot the response.
Can be a vector, for example, col.response!=0.col.response!=0.jitter as the factor argument).
Default 0, no jitter.
A typical useful value is 1.
Applies only if <NULL, meaning do not apply a function.
For example, you could use inverse.func=exp if your
model formula is log(y)~x.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 samFALSE.
Use TRUE to flip the direction of the y axis of the degree2 graphs.FALSE.
Use TRUE to swap the x and y axes on the degree2 graphs.FALSE.
Use TRUE to trace operation.
Use values greater than 1 for more detailed tracing.
The following arguments are for degree1 (main effect) plots0, no rug.
Special value -1 for all, i.e., nrow(x).
Otherwise a random subset of nrug points is taken.se times the pointwise standard errors.
Default is 0, no standard error bands.
A typical value would be 2.
The predict method for object must suse shading. Default is "lightblue".
Use 0 for no shading.se lines. Default is 0, no lines just shading.se lines. Default is 2.func(x) if func is not NULL.
Default is NULL.
This is useful if you are comparing the model to a known function.
The func is called with a single argument which
func points.
Default is "pink".func plot.
Default is 1.func plot.
Default is 1.degree1 plots.
Default is 500.degree1 plots. Default is 1.degree1 plots. Default is 1.
The following arguments are for degree2 plotsdegree2 plots (ngrid2 x ngrid2 points are plotted).
Default is 20.
Note 1: the default will often be too small for contour and image plots.
Note 2: with large ngrid2image plot. Default is grey(0:9/10).
The default excludes grey(1) (white) because that is
the color of clipped values, see clip.persp surface. Default is "lightblue".
Use 0 for no color.persp.
Default is NA, meaning automatically rotate each graph
so the highest corner is furthest away.
(Use trace=TRUE to see the calculated valupersp. Default is 30.persp. Default is 0.5.
The following settings are related to par().TRUE, meaning
call par() as appropriate for settings such as mfrow.
Use FALSE if you don't want plotmo to start a new page.call and response name.caption, for the overall title.degree1 plots
(for degree2 plots the abscissa labels are always the variable names).
Default is "", no label, which gives more plottable area.
Use the special value NULL<xlab.1.type2.
For persp plots, ticktype="d", nticks=2 is useful.Plotmo can be used on a wide variety of regression models.
It plots a degree1 plot by calling predict to
predict the response when changing one variable while holding all
other variables at their median values.
For degree2 plots, two variables are changed while holding others
at their medians.
For factors, the first level is used instead of the median.
You can change this value with the grid.func and
grid.levels arguments.Each graph shows only a thin slice of the data because most variables are fixed. Please be aware of that when interpreting the graph --- over-interpretation is a temptation.
The name plotmo was chosen because it is short, pronounceable as a word,
yet unlikely to conflict with names in other packages or user code.
Plotmo was originally part of the earth package
and a few connections to that package still remain.
Limitations
NAs are not supported.
To prevent confusing error messages from functions called by plotmo,
it is safest to remove NAs before building your model.
(However, rpart models are treated specially by
plotmo, actually allowing NAs so you can use plotmo with
the default arguments for rpart.)
Keep the variable names in the original model formula simple.
Use temporary variables or attach rather than using
$ and similar in formulas.
Plotmo assumes that the data used to build the model is still
available when plotmo is called.
Which variables get plotted?
Plotmo invokes object-specific methods to select which variables to plot.
The set of variables plotted for some common classes is listed below.
This set may leave out pairs that you would like to see ---
in that case use all2=TRUE.
[object Object],[object Object],[object Object],[object Object],[object Object]
Using plotmo on various models
Here are some examples which illustrate plotmo on various
objects. The models are just for illustrating plotmo and
shouldn't be taken too seriously.
# use a small set of variables for illustration
library(earth) # for ozone1 data
data(ozone1)
oz <- ozone1[, c("O3", "humidity", "temp", "ibt")]
lm.fit <- lm(O3 ~ humidity + temp*ibt, data=oz) # linear model plotmo(lm.fit, se=2, col.response=2, nrug=-1)
library(mgcv) # GAM gam.fit <- gam(O3 ~ s(humidity) + s(temp) + s(ibt) + s(temp, ibt), data=oz) plotmo(gam.fit, se=2, all2=TRUE)
library(rpart) # rpart rpart.fit <- rpart(O3 ~ ., data=oz) plotmo(rpart.fit, all2=TRUE)
library(randomForest) # randomForest rf.fit <- randomForest(O3~., data=oz) plotmo(rf.fit) partialPlot(rf.fit, oz, temp) # compare partial dependence plot
library(gbm) # gbm gbm.fit <- gbm(O3~., data=oz, dist="gaussian", inter=2, n.trees=1000) plotmo(gbm.fit) plot(gbm.fit, i.var=2) # compare partial dependence plots plot(gbm.fit, i.var=c(2,3))
library(MASS) # qda
lcush <- data.frame(Type=as.numeric(Cushings$Type), log(Cushings[,1:2]))
lcush <- lcush[1:21,]
qda.fit <- qda(Type~., data=lcush)
plotmo(qda.fit, type="class", all2=TRUE,
type2="contour", ngrid2=100, nlevels=2, drawlabels=FALSE,
col.response=as.numeric(lcush$Type)+1,
pch.response=as.character(lcush$Type))
Plotmo has to make some assumptions about the model object. If
the model function did not save the call or data with the object in a
standard fashion, plotmo cannot proceed and will issue an error.
Object-specific methods can be usually written to deal with such
issues, see the next section.
Extending plotmo
Plotmo calls the S3 methods listed below.
The default methods suffice for many objects,
but where necessary plotmo can be extended by writing new methods.
See plotmo.gbm.R for an example.
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Alternatives
An alternative approach is to use partial dependence plots
(e.g. The Elements of Statistical Learning Section 10.13.2).
Plotmo sets the ``other'' variables to their median
value, whereas in a partial dependence plot at each plotted point the
effect of the other variables is averaged.
There appears to be no general-purpose R function for partial
dependence plots similar to plotmo.
Averaging over the sample at every point is a slow process
unless the effect of averaging can be
determined without actually doing the calculation.
That is not the case for most models, but it is for trees.
See partialPlot in the
randomForest package.
Termplot is effective where applicable, but
it can be used only on models with a predict method that
supports type="terms", and it does not generate
degree2 plots.
Common error messages
Error in match.arg(type): 'arg' should be one of ...
The message is probably issued by the predict method for your
object. Set type to an appropriate value. So
if you are plotting an earth object, for example, the appropriate
values for type will be given in the help page for
predict.earth.
Error: predicted values are out of ylim, try clip=FALSE
With clip=TRUE (the default), plotmo sets the range of
the response axis of the graphs to the range of the response y
used when originally building the model.
When plotmo calls predict for each graph, it issues the
above message (or similar) if all the predicted values are out of the range.
Depending on the model, the above approach may be wrong.
For example, if we are predicting log odds, the predicted response
will not be on the same scale as the original response.
Plotmo does know about some special cases. For example, it knows
that for some models we are predicting a probability, and it
scales the axes accordingly. However, not all situations are handled.
Plotmo does not know about every possible model
and prediction type, and
that is typically when the above message is issued.
The remedy is simple: re-invoke plotmo with clip=FALSE.
Error: get.plotmo.x.default cannot get the x matrix
This and similar messages mean that plotmo cannot get the data
it needs from the model object.
Typically this means that class methods need to be written for the
object, see ``Extending plotmo'' above (and contact the author).
Warning in model.frame.default: 'newdata' had 50 rows but vars have 31 rows
This message usually means that model.frame cannot
find all the variables in the data frame created by plotmo.
Make sure the variables you used to build the model are still
available when you call plotmo. Try also simplifying
the formula used to create the model.
Error in model.frame: invalid type (list) for variable 'x[,3]'
Plotmo can get confused by variables in formulas
which use indexing, such as x[,3].
The symptom is usually a message similar to the above.
FAQ
I want to add lines or points to a plot created by plotmo.
and am having trouble getting my axis scaling right. Help?
Use do.par=FALSE.
With do.par=FALSE, the axis scales match the axis labels.
With do.par=TRUE, plotmo restores the
par parameters and axis scales to
their values before plotmo was called.
The persp display is very jagged. How can I change that?
Try using clip=FALSE.
The jaggedness is probably an artifact of the way persp works
at the boundaries.
You can also try increasing ngrid2.
The image display has white ``holes'' in it. What are those?
The ``holes'' are probably areas where the
predicted response is out-of-range.
Try using clip=FALSE.
Why is the default clip = TRUE?
It is a useful sanity check for plotmo to test that the predicted
values are in the expected range.
While not necessarily an error, predictions outside the expected range
are usually something we want to know about.
Also, with clip=FALSE, a few errant predictions can compress the
entire y-axis making it difficult to see the shape of the other
predictions.
www.milbo.org/rpart-plot/prp.pdf has a section on plotmolibrary(rpart)
data(kyphosis)
rpart.model <- rpart(Kyphosis~., data=kyphosis)
plotmo(rpart.model, type="prob", nresponse="present")Run the code above in your browser using DataLab