validate.rpart {rms} | R Documentation |
Uses xval
-fold cross-validation of a sequence of trees to derive
estimates of the mean squared error and Somers' Dxy
rank correlation
between predicted and observed responses. In the case of a binary response
variable, the mean squared error is the Brier accuracy score.
There are print
and plot
methods for
objects created by validate.rpart
.
# f <- rpart(formula=y ~ x1 + x2 + \dots) # or rpart ## S3 method for class 'rpart' validate(fit, method, B, bw, rule, type, sls, aics, force, pr=TRUE, k, rand, xval=10, FUN, ...) ## S3 method for class 'validate.rpart' print(x, ...) ## S3 method for class 'validate.rpart' plot(x, what=c("mse","dxy"), legendloc=locator, ...)
fit |
an object created by |
method,B,bw,rule,type,sls,aics,force |
are there only for consistency with the generic |
x |
the result of |
k |
a sequence of cost/complexity values. By default these are obtained
from calling |
rand |
a random sample (usually omitted) |
xval |
number of splits |
FUN |
the name of a function which produces a sequence of trees, such
|
... |
additional arguments to |
pr |
set to |
what |
a vector of things to plot. By default, 2 plots will be done, one for
|
legendloc |
a function that is evaluated with a single argument equal to |
a list of class "validate.rpart"
with components named k, size, dxy.app
,
dxy.val, mse.app, mse.val, binary, xval
. size
is the number of nodes,
dxy
refers to Somers' D
, mse
refers to mean squared error of prediction,
app
means apparent accuracy on training samples, val
means validated
accuracy on test samples, binary
is a logical variable indicating whether
or not the response variable was binary (a logical or 0/1 variable is
binary). size
will not be present if the user specifies k
.
prints if pr=TRUE
Frank Harrell
Department of Biostatistics
Vanderbilt University
f.harrell@vanderbilt.edu
rpart
, somers2
,
rcorr.cens
, locator
,
legend
## Not run: n <- 100 set.seed(1) x1 <- runif(n) x2 <- runif(n) x3 <- runif(n) y <- 1*(x1+x2+rnorm(n) > 1) table(y) require(rpart) f <- rpart(y ~ x1 + x2 + x3, model=TRUE) v <- validate(f) v # note the poor validation par(mfrow=c(1,2)) plot(v, legendloc=c(.2,.5)) par(mfrow=c(1,1)) ## End(Not run)