# predict.bagEarth

##### Predicted values based on bagged Earth and FDA models

Predicted values based on bagged Earth and FDA models

- Keywords
- regression

##### Usage

```
# S3 method for bagEarth
predict(object, newdata = NULL, type = NULL, ...)
```# S3 method for bagFDA
predict(object, newdata = NULL, type = "class", ...)

##### Arguments

- object
Object of class inheriting from

`bagEarth`

- newdata
An optional data frame or matrix in which to look for variables with which to predict. If omitted, the fitted values are used (see note below).

- type
The type of prediction. For bagged

`earth`

regression model,`type = "response"`

will produce a numeric vector of the usual model predictions.`earth`

also allows the user to fit generalized linear models. In this case,`type = "response"`

produces the inverse link results as a vector. In the case of a binomial generalized linear model,`type = "response"`

produces a vector of probabilities,`type = "class"`

generates a factor vector and`type = "prob"`

produces a two-column matrix with probabilities for both classes (averaged across the individual models). Similarly, for bagged`fda`

models,`type = "class"`

generates a factor vector and`type = "probs"`

outputs a matrix of class probabilities.- …
not used

##### Value

A vector of predictions (for regression or `type = "class"`

)
or a data frame of class probabilities. By default, when the model
predicts a number, a vector of numeric predictions is returned. When
a classification model is used, the default prediction is a factor vector
of classes.

##### Note

If the predictions for the original training set are needed, there are
two ways to calculate them. First, the original data set can be predicted by
each bagged earth model. Secondly, the predictions from each bootstrap
sample could be used (but are more likely to overfit). If the original call
to `bagEarth`

or `bagFDA`

had `keepX = TRUE`

, the first
method is used, otherwise the values are calculated via the second method.

##### See Also

##### Examples

```
# NOT RUN {
# }
# NOT RUN {
data(trees)
## out of bag predictions vs just re-predicting the training set
set.seed(655)
fit1 <- bagEarth(Volume ~ ., data = trees, keepX = TRUE)
set.seed(655)
fit2 <- bagEarth(Volume ~ ., data = trees, keepX = FALSE)
hist(predict(fit1) - predict(fit2))
# }
# NOT RUN {
# }
```

*Documentation reproduced from package caret, version 6.0-86, License: GPL (>= 2)*