`randomForest`

implements Breiman's random forest algorithm (based on
Breiman and Cutler's original Fortran code) for classification and
regression. It can also be used in unsupervised mode for assessing
proximities among data points.

```
# S3 method for formula
randomForest(formula, data=NULL, ..., subset, na.action=na.fail)
# S3 method for default
randomForest(x, y=NULL, xtest=NULL, ytest=NULL, ntree=500,
mtry=if (!is.null(y) && !is.factor(y))
max(floor(ncol(x)/3), 1) else floor(sqrt(ncol(x))),
replace=TRUE, classwt=NULL, cutoff, strata,
sampsize = if (replace) nrow(x) else ceiling(.632*nrow(x)),
nodesize = if (!is.null(y) && !is.factor(y)) 5 else 1,
maxnodes = NULL,
importance=FALSE, localImp=FALSE, nPerm=1,
proximity, oob.prox=proximity,
norm.votes=TRUE, do.trace=FALSE,
keep.forest=!is.null(y) && is.null(xtest), corr.bias=FALSE,
keep.inbag=FALSE, ...)
# S3 method for randomForest
print(x, ...)
```

data

an optional data frame containing the variables in the model.
By default the variables are taken from the environment which
`randomForest`

is called from.

subset

an index vector indicating which rows should be used. (NOTE: If given, this argument must be named.)

na.action

A function to specify the action to be taken if NAs are found. (NOTE: If given, this argument must be named.)

x, formula

a data frame or a matrix of predictors, or a formula
describing the model to be fitted (for the
`print`

method, an `randomForest`

object).

y

A response vector. If a factor, classification is assumed,
otherwise regression is assumed. If omitted, `randomForest`

will run in unsupervised mode.

xtest

a data frame or matrix (like `x`

) containing
predictors for the test set.

ytest

response for the test set.

ntree

Number of trees to grow. This should not be set to too small a number, to ensure that every input row gets predicted at least a few times.

mtry

Number of variables randomly sampled as candidates at each
split. Note that the default values are different for
classification (sqrt(p) where p is number of variables in `x`

)
and regression (p/3)

replace

Should sampling of cases be done with or without replacement?

classwt

Priors of the classes. Need not add up to one. Ignored for regression.

cutoff

(Classification only) A vector of length equal to number of classes. The `winning' class for an observation is the one with the maximum ratio of proportion of votes to cutoff. Default is 1/k where k is the number of classes (i.e., majority vote wins).

strata

A (factor) variable that is used for stratified sampling.

sampsize

Size(s) of sample to draw. For classification, if sampsize is a vector of the length the number of strata, then sampling is stratified by strata, and the elements of sampsize indicate the numbers to be drawn from the strata.

nodesize

Minimum size of terminal nodes. Setting this number larger causes smaller trees to be grown (and thus take less time). Note that the default values are different for classification (1) and regression (5).

maxnodes

Maximum number of terminal nodes trees in the forest
can have. If not given, trees are grown to the maximum possible
(subject to limits by `nodesize`

). If set larger than maximum
possible, a warning is issued.

importance

Should importance of predictors be assessed?

localImp

Should casewise importance measure be computed?
(Setting this to `TRUE`

will override `importance`

.)

nPerm

Number of times the OOB data are permuted per tree for assessing variable importance. Number larger than 1 gives slightly more stable estimate, but not very effective. Currently only implemented for regression.

proximity

Should proximity measure among the rows be calculated?

oob.prox

Should proximity be calculated only on ``out-of-bag'' data?

norm.votes

If `TRUE`

(default), the final result of votes
are expressed as fractions. If `FALSE`

, raw vote counts are
returned (useful for combining results from different runs).
Ignored for regression.

do.trace

If set to `TRUE`

, give a more verbose output as
`randomForest`

is run. If set to some integer, then running
output is printed for every `do.trace`

trees.

keep.forest

If set to `FALSE`

, the forest will not be
retained in the output object. If `xtest`

is given, defaults
to `FALSE`

.

corr.bias

perform bias correction for regression? Note: Experimental. Use at your own risk.

keep.inbag

Should an `n`

by `ntree`

matrix be
returned that keeps track of which samples are ``in-bag'' in which
trees (but not how many times, if sampling with replacement)

...

optional parameters to be passed to the low level function
`randomForest.default`

.

An object of class `randomForest`

, which is a list with the
following components:

the original call to `randomForest`

one of `regression`

, `classification`

, or
`unsupervised`

.

the predicted values of the input data based on out-of-bag samples.

a matrix with `nclass`

+ 2 (for classification)
or two (for regression) columns. For classification, the first
`nclass`

columns are the class-specific measures computed as
mean descrease in accuracy. The `nclass`

+ 1st column is the
mean descrease in accuracy over all classes. The last column is the
mean decrease in Gini index. For Regression, the first column is
the mean decrease in accuracy and the second the mean decrease in MSE.
If `importance=FALSE`

, the last measure is still returned as a
vector.

The ``standard errors'' of the permutation-based
importance measure. For classification, a `p`

by ```
nclass
+ 1
```

matrix corresponding to the first `nclass + 1`

columns
of the importance matrix. For regression, a length `p`

vector.

a p by n matrix containing the casewise importance
measures, the [i,j] element of which is the importance of i-th
variable on the j-th case. `NULL`

if `localImp=FALSE`

.

number of trees grown.

number of predictors sampled for spliting at each node.

(a list that contains the entire forest; `NULL`

if
`randomForest`

is run in unsupervised mode or if
`keep.forest=FALSE`

.

(classification only) vector error rates of the prediction on the input data, the i-th element being the (OOB) error rate for all trees up to the i-th.

(classification only) the confusion matrix of the prediction (based on OOB data).

(classification only) a matrix with one row for each input data point and one column for each class, giving the fraction or number of (OOB) `votes' from the random forest.

number of times cases are `out-of-bag' (and thus used in computing OOB error estimate)

if `proximity=TRUE`

when
`randomForest`

is called, a matrix of proximity measures among
the input (based on the frequency that pairs of data points are in
the same terminal nodes).

(regression only) vector of mean square errors: sum of squared
residuals divided by `n`

.

(regression only) ``pseudo R-squared'': 1 - `mse`

/
Var(y).

if test set is given (through the `xtest`

or additionally
`ytest`

arguments), this component is a list which contains the
corresponding `predicted`

, `err.rate`

, `confusion`

,
`votes`

(for classification) or `predicted`

, `mse`

and
`rsq`

(for regression) for the test set. If
`proximity=TRUE`

, there is also a component, `proximity`

,
which contains the proximity among the test set as well as proximity
between test and training data.

Breiman, L. (2001), *Random Forests*, Machine Learning 45(1),
5-32.

Breiman, L (2002), ``Manual On Setting Up, Using, And Understanding Random Forests V3.1'', https://www.stat.berkeley.edu/~breiman/Using_random_forests_V3.1.pdf.

# NOT RUN { ## Classification: ##data(iris) set.seed(71) iris.rf <- randomForest(Species ~ ., data=iris, importance=TRUE, proximity=TRUE) print(iris.rf) ## Look at variable importance: round(importance(iris.rf), 2) ## Do MDS on 1 - proximity: iris.mds <- cmdscale(1 - iris.rf$proximity, eig=TRUE) op <- par(pty="s") pairs(cbind(iris[,1:4], iris.mds$points), cex=0.6, gap=0, col=c("red", "green", "blue")[as.numeric(iris$Species)], main="Iris Data: Predictors and MDS of Proximity Based on RandomForest") par(op) print(iris.mds$GOF) ## The `unsupervised' case: set.seed(17) iris.urf <- randomForest(iris[, -5]) MDSplot(iris.urf, iris$Species) ## stratified sampling: draw 20, 30, and 20 of the species to grow each tree. (iris.rf2 <- randomForest(iris[1:4], iris$Species, sampsize=c(20, 30, 20))) ## Regression: ## data(airquality) set.seed(131) ozone.rf <- randomForest(Ozone ~ ., data=airquality, mtry=3, importance=TRUE, na.action=na.omit) print(ozone.rf) ## Show "importance" of variables: higher value mean more important: round(importance(ozone.rf), 2) ## "x" can be a matrix instead of a data frame: set.seed(17) x <- matrix(runif(5e2), 100) y <- gl(2, 50) (myrf <- randomForest(x, y)) (predict(myrf, x)) ## "complicated" formula: (swiss.rf <- randomForest(sqrt(Fertility) ~ . - Catholic + I(Catholic < 50), data=swiss)) (predict(swiss.rf, swiss)) ## Test use of 32-level factor as a predictor: set.seed(1) x <- data.frame(x1=gl(53, 10), x2=runif(530), y=rnorm(530)) (rf1 <- randomForest(x[-3], x[[3]], ntree=10)) ## Grow no more than 4 nodes per tree: (treesize(randomForest(Species ~ ., data=iris, maxnodes=4, ntree=30))) ## test proximity in regression iris.rrf <- randomForest(iris[-1], iris[[1]], ntree=101, proximity=TRUE, oob.prox=FALSE) str(iris.rrf$proximity) # }