R: Predictions from a Fitted Tree Object

predict.tree {tree} | R Documentation

## Predictions from a Fitted Tree Object

### Description

Returns a vector of predicted responses from a fitted tree object.

### Usage

    ## S3 method for class 'tree'
    predict(object, newdata = list(),
            type = c("vector", "tree", "class", "where"),
            split = FALSE, nwts, eps = 1e-3, ...)

### Arguments

`object` | fitted model object of class `tree`. This is assumed to be the result of some function that produces an object with the same named components as that returned by the `tree` function.
`newdata` | data frame containing the values at which predictions are required. The predictors referred to in the right side of `formula(object)` must be present by name in `newdata`. If missing, fitted values are returned.
`type` | character string denoting whether the predictions are returned as a vector (default) or as a tree object.
`split` | governs the handling of missing values. If false, cases with missing values are dropped down the tree until a leaf is reached or a node for which the attribute is missing, and that node is used for prediction. If `split = TRUE` cases with missing attributes are split into fractional cases and dropped down each side of the split. The predicted values are averaged over the fractions to give the prediction.
`nwts` | weights for the `newdata` cases, used when predicting a tree.
`eps` | a lower bound for the probabilities, used if events of predicted probability zero occur in `newdata` when predicting a tree.
`...` | further arguments passed to or from other methods.

### Details

This function is a method for the generic function `predict()` for class `tree`. It can be invoked by calling `predict(x)` for an object `x` of the appropriate class, or directly by calling `predict.tree(x)` regardless of the class of the object.

### Value

If `type = "vector"`: vector of predicted responses or, if the response is a factor, matrix of predicted class probabilities. This new object is obtained by dropping `newdata` down `object`. For factor predictors, if an observation contains a level not used to grow the tree, it is left at the deepest possible node and `frame$yval` or `frame$yprob` at that node is the prediction.

If `type = "tree"`: an object of class `"tree"` is returned with new values for `frame$n` and `frame$dev`. If `newdata` does not contain a column for the response in the formula the value of `frame$dev` will be `NA`, and if some values in the response are missing, the some of the deviances will be `NA`.

If `type = "class"`: for a classification tree, a factor of the predicted classes (that with highest posterior probability, with ties split randomly).

If `type = "where"`: the nodes the cases reach.

### References

Ripley, B. D. (1996). Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge. Chapter 7.

### See Also

`predict`, `tree`.

### Examples

    data(shuttle, package="MASS")
    shuttle.tr <- tree(use ~ ., shuttle, subset=1:253,
                       mindev=1e-6, minsize=2)
    shuttle.tr
    shuttle1 <- shuttle[254:256, ]  # 3 missing cases
    predict(shuttle.tr, shuttle1)

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