| randomVarImpsRFplot {varSelRF} | R Documentation |
Plot variable importances from random permutations of class labels and the variable importances from the original data set.
randomVarImpsRFplot(randomImportances, forest,
whichImp = "impsUnscaled", nvars = NULL,
show.var.names = FALSE, vars.highlight = NULL,
main = NULL, screeRandom = TRUE,
lwdBlack = 1.5,
lwdRed = 2,
lwdLightblue = 1,
cexPoint = 1,
overlayTrue = FALSE,
xlab = NULL,
ylab = NULL, ...)
randomImportances |
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. |
Only used for its side effects of producing plots. In particular, you will see lines of three colors:
black |
|
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.
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.
Ramon Diaz-Uriarte rdiaz@ligarto.org
Breiman, L. (2001) Random forests. Machine Learning, 45, 5–32.
Diaz-Uriarte, R. , Alvarez de Andres, S. (2005) Variable selection from random forests: application to gene expression data. Tech. report. http://ligarto.org/rdiaz/Papers/rfVS/randomForestVarSel.html
Friedman, J., Meulman, J. (2005) Clustering objects on subsets of attributes (with discussion). J. Royal Statistical Society, Series B, 66, 815–850.
randomForest,
varSelRF,
varSelRFBoot,
varSelImpSpecRF,
randomVarImpsRF
x <- matrix(rnorm(45 * 30), ncol = 30)
x[1:20, 1:2] <- x[1:20, 1:2] + 2
cl <- factor(c(rep("A", 20), rep("B", 25)))
rf <- randomForest(x, cl, ntree = 200, importance = TRUE)
rf.rvi <- randomVarImpsRF(x, cl,
rf,
numrandom = 20,
usingCluster = FALSE)
randomVarImpsRFplot(rf.rvi, rf)