plotmo(object = stop("no 'object' arg"),
type=NULL, nresponse = NA, clip = TRUE, ylim = NULL,
center = FALSE, ndiscrete = 5,
degree1 = TRUE, all1=FALSE, degree2 = TRUE, all2=FALSE, int.only.ok = TRUE,
grid.func = median, grid.levels = NULL,
col.response = 0, cex.response = NULL, pch.response = 20,
jitter.response=0, npoints = -1, inverse.func = NULL,
level = 0, shade.pints = "mistyrose2", shade2.pints = "mistyrose4",
trace = 0,
nrug = 0, col.degree1 = 1, lty.degree1 = 1, lwd.degree1 = 1,
col.smooth = 0, lty.smooth = 1, lwd.smooth = 1, smooth.f = .5,
func = NULL, col.func = "lightblue", lty.func = 1, lwd.func = 1,
ngrid1 = 50, grid = FALSE,
type2 = "persp", ngrid2 = 20,
col.image = gray(0:10/10), col.persp = "lightblue",
theta = NA, phi = 30, dvalue = 1, shade = 0.5,
do.par = TRUE, caption = NULL, main = NULL,
xlim = NULL, xlab = "", ylab = "", cex = NULL, cex.lab = 1,
xflip = FALSE, yflip = FALSE, swapxy = FALSE, se = 0, ...)plotmo,
it is safest to remove NAs before building your model.
(However, rpart models are treated specially by
plotmo. For these, plotmo predicts with na.pass
so plotmo can be used with rpart's default NA handling.)Keep the variable names in the original model formula simple.
Use temporary variables or attach rather than using
$ and similar in formulas.
Plotmo evaluates the model data in the environment
used when the model was built, if that environment was saved with the model
(typically this is the case if the formula interface was used to the
model function). If the environment was not saved with the model
(typically if the x,y interface was used),
the model data is evaluated in the environment in which plotmo
is called.
Plotmo sets the plotmo plots will
differ, but for additive models the shape of the curves will
match identically.
Eventually plotmo will be enhanced to draw
partial-dependence plots.
Termplot is effective but can be used only on
models with a predict method that supports type="terms",
and it does not generate degree2 plots.Some other possibilites for plotting the response on a per-predictor
basis are partial-residual plots, partial-regression variable plots, and
marginal-model plots (e.g. crPlots,
avPlots, and marginalModelPlot in the
car-package).
These plots are orientated towards linear models.
The effects-package package is also of interest.
clip=TRUE, predicted values out of the
expected range are not displayed.Generally, the y used when building the model.
But that depends on the type of model, and plotmo
knows about some special cases.
For example, it knows that for some models we are predicting a
probability, and it scales the axes accordingly, 0 to 1.
However, plotmo cannot know about every possible model and
prediction type, and will sometimes determine the expected response range
incorrectly.
In that case use clip=FALSE.
The default clip is TRUE because it is a useful sanity
check 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 expand the
entire y-axis, making it difficult to see the shape of the other
predictions.
plotmo on various
objects.
(The models here 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.model <- lm(O3 ~ humidity + temp*ibt, data=oz) # linear model plotmo(lm.model, col.response="gray", nrug=-1)
library(rpart) # rpart rpart.model <- rpart(O3 ~ ., data=oz) plotmo(rpart.model, all2=TRUE)
library(randomForest) # randomForest rf.model <- randomForest(O3~., data=oz) plotmo(rf.model) # partialPlot(rf.model, oz, temp) # compare to partial-dependence plot
library(gbm) # gbm gbm.model <- gbm(O3~., data=oz, dist="gaussian", inter=2, n.trees=1000) plotmo(gbm.model) # plot(gbm.model, i.var=2) # compare to partial-dependence plots # plot(gbm.model, i.var=c(2,3))
library(mgcv) # gam gam.model <- gam(O3 ~ s(humidity)+s(temp)+s(ibt)+s(temp,ibt), data=oz) plotmo(gam.model, level=.95, all2=TRUE)
library(nnet) # nnet set.seed(4) nnet.model <- nnet(O3~., data=scale(oz), size=2, decay=0.01, trace=FALSE) plotmo(nnet.model, type="raw", all2=T)
library(MASS) # qda lcush <- data.frame(Type=as.numeric(Cushings$Type),log(Cushings[,1:2])) lcush <- lcush[1:21,] qda.model <- qda(Type~., data=lcush) plotmo(qda.model, type="class", all2=TRUE, type2="contour", ngrid2=100, nlevels=2, drawlabels=FALSE, col.response=as.numeric(lcush$Type)+1, pch.response=as.character(lcush$Type))
level argument to plot pointwise confidence or prediction intervals.
The predict method of the object must support this.
Examples:
par(mfrow=c(2,3))
log.trees <- log(trees) # make the resids more homoscedastic
# (necessary for lm)# lm lm.model <- lm(Volume~Height, data=log.trees) plot(lm.model, which=1, main="lm") # Residual vs Fitted graph plotmo(lm.model, level=.90, col.response=2, main="lm\n(conf and pred intervals)", do.par=F)
# earth (requires earth 3.3) library(earth) earth.model <- earth(Volume~Height, data=log.trees, nfold=5, ncross=30, varmod.method="lm") plotmo(earth.model, level=.90, col.response=2, main="earth", do.par=F)
# quantreg library(quantreg) rq.model <- rq(Volume~Height, data=log.trees, tau=c(.05, .5, .95)) plotmo(rq.model, level=.90, col.response=2, main="rq", do.par=F)
# quantregForest # quantregForest is a layer on randomForest that allows prediction intervals library(quantregForest) x <- data.frame(Height=log.trees$Height) qrf <- quantregForest(x, log.trees$Volume) plotmo(qrf, level=.90, col.response=2, main="qrf", do.par=F)
# gam library(mgcv) gam.model <- gam(Volume~s(Height), data=log.trees) plotmo(gam.model, level=.90, col.response=2, main="gam\n(conf not pred intervals)", do.par=F)
Error in match.arg(type): 'arg' should be one of ...The message is probably issued by the predict method for your
model object.
Set type to a legal value as described
on the help page for the predict method for your object.oError: predicted values are out of ylim, try clip=FALSE
Probably plotmo has incorrectly determined the expected range
of the response, and hence also ylim.
Re-invoke plotmo with clip=FALSE.
See the section clip argumentError: predict.lm(xgrid, type="response") returned the wrong length
oWarning: 'newdata' had 100 rows but variable(s) found have 30 rows
oError: variable 'x' was fitted with type "nmatrix.2" but type "numeric" was supplied
oError in model.frame: invalid type (list) for variable 'x[,3]'
These and similar messages usually mean that predict is
misinterpreting the new data generated by plotmo.
The underlying issue is that many predict methods, including
predict.lm, seem to reject any reasonably constructed new data
if the function used to create the model was called in an
unconventional way.
The work-around is to simplify or standardize the
way the model function is called. Use a formula and a data frame, or
at least explicitly name the variables rather than passing a
matrix. Use simple variable names (so x1 rather than
dat$x1, for example).
If the symptoms persist after changing the way the model is called,
and the model is not one of those listed in plotmo. See 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.
You can try simplifying and standardizing the way the
model function is called, as described above.
Perhaps you need to use keepxy or similar in the call to the
model function, so the data is attached to the object and available for
plotmo.
Is a variable that was used to build the model no longer available in
the environment when plotmo is called?oError: this object is not supported by plotmo
Plotmo's default methods are insufficient for your model object.
See
Use all2=TRUE.
By default, degree2 plots are drawn only for some types of model.
See the section
o The persp display is unnaturally jagged. How can I change that?
Use clip=FALSE.
The jaggedness is probably an artifact of the way persp works
at the boundaries.
You can also try increasing ngrid2.
o The image display has blue
The holes are areas where the predicted response is out-of-range.
Try using clip=FALSE.
o 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 or do.par=2.
With the default do.par=TRUE, plotmo restores the
par parameters and axis scales to
their values before plotmo was called.
Plotmo can be used on a wide variety of regression models.
It plots a degree1 (main effect) plot by calling predict to
predict the response when changing one variable while holding all
other variables at their median values.
For degree2 (interaction) plots, two variables are changed while holding others
at their medians.
The first level is used instead of the median for factors.
You can change this 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.
Plotmo was originally part of the earth package
and a few connections to that package still remain.
plotmo in the rpart.plot vignette
rpart trees with prp}if (require(rpart)) {
data(kyphosis)
rpart.model <- rpart(Kyphosis~., data=kyphosis)
plotmo(rpart.model, type="prob", nresponse="present")
}
if (require(earth)) {
data(ozone1)
earth.model <- earth(O3 ~ ., data=ozone1, degree=2, nk=21)
plotmo(earth.model)
}Run the code above in your browser using DataLab