If `object$type`

is `regression`

, a vector of predicted
values is returned. If `predict.all=TRUE`

, then the returned
object is a list of two components: `aggregate`

, which is the
vector of predicted values by the forest, and `individual`

, which
is a matrix where each column contains prediction by a tree in the
forest.

If `object$type`

is `classification`

, the object returned
depends on the argument `type`

:

- response
predicted classes (the classes with majority vote).

- prob
matrix of class probabilities (one column for each class
and one row for each input).

- vote
matrix of vote counts (one column for each class
and one row for each new input); either in raw counts or in fractions
(if `norm.votes=TRUE`

).

If `predict.all=TRUE`

, then the `individual`

component of the
returned object is a character matrix where each column contains the
predicted class by a tree in the forest.

If `proximity=TRUE`

, the returned object is a list with two
components: `pred`

is the prediction (as described above) and
`proximity`

is the proximitry matrix. An error is issued if
`object$type`

is `regression`

.

If `nodes=TRUE`

, the returned object has a ``nodes'' attribute,
which is an n by ntree matrix, each column containing the node number
that the cases fall in for that tree.

NOTE: If the `object`

inherits from `randomForest.formula`

,
then any data with `NA`

are silently omitted from the prediction.
The returned value will contain `NA`

correspondingly in the
aggregated and individual tree predictions (if requested), but not in
the proximity or node matrices.

NOTE2: Any ties are broken at random, so if this is undesirable, avoid it by
using odd number `ntree`

in `randomForest()`

.