plotmo(object = stop("no 'object' arg"),
degree1 = TRUE, degree2 = TRUE, sub.caption = if(do.par) NULL else "",
ylim = NULL, clip = TRUE, col.response = 0, pch.response = 1,
trace = FALSE, inverse.func = NULL, grid.func = median,
ndegree1 = 500, lty.degree1 = 1, col.degree1 = 1,
se = 0, col.shade = "lightblue", col.se = 0, lty.se = 2,
func = NULL, col.func = "pink", pch.func = 20, nrug = 0,
type2 = "persp", ngrid = 20,
col.persp = "lightblue", col.image = grey(0:9/10),
do.par = TRUE, main = NULL, theta = NA, phi = 30, shade = 0.5,
ticktype = "simple", xlab = "", ylab = "", cex = NULL, ...)TRUE, meaning all.
Perhaps the easiest way to use this argument (and degree2) is to
first plot all figures to see how the figures are numbered, then
TRUE, meaning all.if(do.par) NULL else "". Values are:
"string" string
"" no caption
NULL generate a caption from object$call.TRUE to trace operation. Default is FALSE.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 y across all (degree1 and degree2) plots.
If col.response!=0 then the reTRUE.persp col.response.
Default is 1.NULL, meaning do not apply a function.
For example, you could use inverse.func=exp if your
model formula is log(y)~x.Note, however
median.
Examples:
plotmo500.
Special value -1 means use nrow(x).1.1.0 meaning no rug.
Special value -1 for all i.e. nrow(x).se times the pointwise standard errors.
Default is 0, meaning no standard error bands.
A typical value is 2.
The predict method for object must se shading. Default is "lightblue".
Use 0 for no shading.se lines. Default is 0 meaning 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. Note that func is called with a single argument w
func points.
Default is "pink".func points.
Default is 20.
The following arguments are for degree2 plots"persp" (default), "contour", or "image".persp surface. Default is "lightblue".
Use 0 for no colour.image plot. Default is grey(0:9/10).
The default excludes 1 because that is the colour of clipped
values, see clip.20.
The following settings are related to par() and are included so you can override the defaults.par() for global settings as appropriate.
Default is TRUE.
Set to FALSE if you want to append figures to an existing plot.NULL, meaning generate figure headings automatically.persp.
Default is NA, meaning
let this function choose the rotation (it rotates each graph so
the highest corner is furthest away).persp. Default is 30.persp. Default is 0.5.persp plot. One of simple or detailed.
Default is "simple"."", meaning none, which gives more plottable area.
Use NULL to use the predictor names as la"", meaning none, which gives more plottable area.plotmo.
Using arguments here may cause warnings which can often be safely ignored.Plotmo is a general purpose model plotting function,
but comes with the earth package.Note that plotmo calls predict with new data and
type="response", whereas termplot
calls predict with type="terms".
Limitations
NAs are not yet allowed.
To prevent confusing error messages from plotmo,
a safe stategy is to build your model with
na.action=na.fail before calling plotmo.
Weights are currently ignored, with a warning.
Factors are not plotted in degree2 plots.
By default (i.e. when using get.x.default and get.pairs.default),
plotmo parses the input formula using gsub.
This crude approach is not infallible but works for the common formulae.
It determines which predictors are paired by looking for
forms such as "x1:x2" or "x1*x2" in the model formula.
Variable names containing $ are not supported.
Plotmo can get confused by predictors in formulae which use indexing, such as x[,3].
The symptom is usually a message along the lines
Error in model.frame: invalid type (list) for variable 'x[,3]'.
A mesage like
Warning in model.frame.default: 'newdata' had 50 rows but variable(s) found have 31 rows
means that model.frame.default cannot find all the variables in the data frame
created by plotmo.
Details of Operation
Let's say the model object has three predictors,
x1, x2, and x3 (all numeric)
and plotmo is about to plot the degree1 plot for x2.
Plotmo first builds an input matrix
with ndegree1 rows and with column names x1, x2, and x3.
It sets all entries for the x1 column to x1's median value (actually,
the value returned by grid.func applied to x1).
Likewise for the x3 column.
It sets the x2 column to an equally spaced sequence of values
from min(x2) to max(x2).
Finally, it calls predict(type="response") with the newly created input matrix,
and plots the predicted values against the sequence of x2 values.
Operation is similar for degree2 plots: all columns of the input
matrix for predict are set to their medians except for the columns of the two
predictors being plotted.
Minimum Requirements
Plotmo requires the following of the model object.
These requirements are for default operation, which can be changed
as described in the next section.
1) object must have a predict method that supports type=response.
2) for standard error bands (see the se argument), object must have a predict
method that can be called with se.fit=TRUE.
3) object must have the following components
(which are searched for in the order given):
a) $call$formula (formula is required for degree2 plots),
$call$x with named columns, or $x with named columns.
b) $call$formula, or $call$y, or $y.
Extending plotmo
Plotmo calls the following generic functions, all defined in plotmo.R:
plotmo.prolog
get.x
get.y
plotmo.predict (the default simply calls predict)
get.singles
get.pairs
Thus plotmo can be extended
by writing new method functions, although the default
functions may suffice for your object's class.
See the examples below and see the source comments for details.
termplot,
plot.earth,
plot.earth.modelsdata(ozone1)
a <- earth(O3 ~ ., data = ozone1, degree = 2)
plotmo(a)
# example with some arguments:
# plotmo(a, sub.caption = "example", ylim = NULL, degree1=c(1,2,4),
# degree2 = 4, col.response = 3, clip = FALSE, ticktype = "d", theta = -30)
# examples using functions other than earth:
#
# plotmo(lm(O3 ~ log(temp) + humidity*temp, data=ozone1), se=2)
#
# library(gam)
# data(airquality)
# airquality <- na.omit(airquality) # plotmo doesn't know how to deal with NAs
# plotmo(gam(Ozone^(1/3) ~ lo(Solar.R) + lo(Wind, Temp), data = airquality))
#
# library(mgcv)
# plotmo(gam(O3 ~ s(doy) + s(humidity,temp), data=ozone1), se=2, ylim=NA)Run the code above in your browser using DataLab