A list with a structure such as the object
return by randomVarImpsRF
.
forest
A random forest fitted to the original data. This forest
must have been fitted with importances = TRUE.
whichImp
The importance measue to use. One (only one) of
impsUnscaled,
impsScaled, impsGini, that correspond, respectively, to
the (unscaled) mean decrease in accuracy, the scaled mean decrease
in accuracy, and the Gini index. See below and
randomForest,
importance and the references for further explanations of the
measures of variable importance.
nvars
If NULL will show the plot for the complete range of
variables. If an integer, will plot only the most important nvars.
show.var.names
If TRUE, show the variable names in the
plot. Unless you are plotting few variables, it probably won't be of
any use.
vars.highlight
A vector indicating the variables to highlight
in the plot with a vertical blue segment. You need to pass here a
vector of variable names, not variable positions.
main
The title for the plot.
screeRandom
If TRUE, order all the variable importances (i.e.,
those from both the original and the permuted class labels data
sets) from
largest to smallest before plotting. The plot will thus resemble a
usual "scree plot".
lwdBlack
The width of the line to use for the importances from
the original data set.
lwdRed
The width of the line to use for the average of the
importances for the permuted data sets.
lwdLightblue
The width of the line for the importances for the
individual permuted data sets.
cexPoint
cex argument for the points for the
importances from the original data set.
overlayTrue
If TRUE, the variable importance from the
original data set will be plotted last, so
you can see it even if buried in the middle of many gree lines; can
be of help when the plot does not allow you to see the black line.
xlab
The title for the x-axis (see xlab).
ylab
The title for the y-axis (see ylab).
...
Additional arguments to plot.
Value
Only used for its side effects of producing plots. In particular, you
will see lines of three colors:
black
Connects the variable importances from the original
simulated data.
green
Connect the variable
importances from the data sets with permuted class labels; there
will be as many lines as numrandom where used when
randomVarImpsRF was called to obtain the random
importances.
red
Connects the average of the importances from the permuted
data sets.
Additionally, if you used a valid set of values for
vars.highlight, these will be shown with a vertical blue
segment.
Note
These plots resemble the scree plots commonly used with principal
component analysis, and the actual choice of colors was taken from the
importance spectrum plots of Friedman & Meulman.