Description Usage Arguments Value Methods References Examples
Compute metric scaling co-ordinates for training examples in a random forest, based on the proximity matrix generated by proximities
. Scaling co-ordinates are useful for visualising the data.
1 2 |
prox |
A proximity matrix of class |
nscale |
The number of scaling co-ordinates to compute. Typically, the first two or three scaling co-ordinates are the most useful. Default: |
trace |
|
A matrix
containing the scaling co-ordinates for each example, where the i
th column contains the i
th scaling co-ordinates.
signature(prox = "bigrfprox")
Compute metric scaling coordinates for a random forest.
Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.
Breiman, L. & Cutler, A. (n.d.). Random Forests. Retrieved from http://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | # Classify cars in the Cars93 data set by type (Compact, Large,
# Midsize, Small, Sporty, or Van).
# Load data.
data(Cars93, package="MASS")
x <- Cars93
y <- Cars93$Type
# Select variables with which to train model.
vars <- c(4:22)
# Run model, grow 30 trees.
forest <- bigrfc(x, y, ntree=30L, varselect=vars, cachepath=NULL)
# Calculate proximity matrix.
prox <- proximities(forest, cachepath=NULL)
# Calculate scaling co-ordinates.
scale <- scaling(prox, 3L)
# Plot the 1st vs 2nd scaling co-ordinates.
plot(scale[, 1], scale[, 2], col=as.integer(y), pch=as.integer(y))
# Plot the 1st vs 3rd scaling co-ordinates.
plot(scale[, 1], scale[, 3], col=as.integer(y), pch=as.integer(y))
# Plot the 2nd vs 3rd scaling co-ordinates.
plot(scale[, 2], scale[, 3], col=as.integer(y), pch=as.integer(y))
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