% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/extractPrediction.R, R/extractProb.R,
%   R/predict.train.R
\name{extractPrediction}
\alias{extractPrediction}
\alias{extractProb}
\alias{predict.train}
\alias{predict.list}
\title{Extract predictions and class probabilities from train objects}
\usage{
extractPrediction(
  models,
  testX = NULL,
  testY = NULL,
  unkX = NULL,
  unkOnly = !is.null(unkX) & is.null(testX),
  verbose = FALSE
)

extractProb(
  models,
  testX = NULL,
  testY = NULL,
  unkX = NULL,
  unkOnly = !is.null(unkX) & is.null(testX),
  verbose = FALSE
)

\method{predict}{train}(object, newdata = NULL, type = "raw", na.action = na.omit, ...)
}
\arguments{
\item{models}{a list of objects of the class \code{train}. The objects must
have been generated with \code{fitBest = FALSE} and \code{returnData =
TRUE}.}

\item{testX}{an optional set of data to predict}

\item{testY}{an optional outcome corresponding to the data given in
\code{testX}}

\item{unkX}{another optional set of data to predict without known outcomes}

\item{unkOnly}{a logical to bypass training and test set predictions. This
is useful if speed is needed for unknown samples.}

\item{verbose}{a logical for printing messages}

\item{object}{For \code{predict.train}, an object of class
\code{\link{train}}. For \code{predict.list}, a list of objects of class
\code{\link{train}}.}

\item{newdata}{an optional set of data to predict on. If \code{NULL}, then
the original training data are used but, if the \code{train} model used a
recipe, an error will occur.}

\item{type}{either "raw" or "prob", for the number/class predictions or
class probabilities, respectively. Class probabilities are not available for
all classification models}

\item{na.action}{the method for handling missing data}

\item{\dots}{only used for \code{sort} and \code{modelCor} and captures
arguments to pass to \code{sort} or \code{FUN}.}
}
\value{
For \code{predict.train}, a vector of predictions if \code{type = "raw"} or
a data frame of class probabilities for \code{type = "prob"}. In the latter
case, there are columns for each class.

For \code{predict.list}, a list results. Each element is produced by
\code{predict.train}.

For \code{extractPrediction}, a data frame with columns: \item{obs }{the
observed training and test data} \item{pred }{predicted values}
\item{model}{the type of model used to predict} \item{object}{the names of
the objects within \code{models}. If \code{models} is an un-named list, the
values of \code{object} will be "Object1", "Object2" and so on}
\item{dataType }{"Training", "Test" or "Unknown" depending on what was
specified}

For \code{extractProb}, a data frame. There is a column for each class
containing the probabilities. The remaining columns are the same as above
(although the \code{pred} column is the predicted class)
}
\description{
These functions can be used for a single \code{train} object or to loop
through a number of \code{train} objects to calculate the training and test
data predictions and class probabilities.
}
\details{
These functions are wrappers for the specific prediction functions in each
modeling package. In each case, the optimal tuning values given in the
\code{tuneValue} slot of the \code{finalModel} object are used to predict.

To get simple predictions for a new data set, the \code{predict} function
can be used. Limits can be imposed on the range of predictions. See
\code{\link{trainControl}} for more information.

To get predictions for a series of models at once, a list of
\code{\link{train}} objects can be passes to the \code{predict} function and
a list of model predictions will be returned.

The two extraction functions can be used to get the predictions and observed
outcomes at once for the training, test and/or unknown samples at once in a
single data frame (instead of a list of just the predictions). These objects
can then be passes to \code{\link{plotObsVsPred}} or
\code{\link{plotClassProbs}}.
}
\examples{

   \dontrun{

knnFit <- train(Species ~ ., data = iris, method = "knn",
                trControl = trainControl(method = "cv"))

rdaFit <- train(Species ~ ., data = iris, method = "rda",
                trControl = trainControl(method = "cv"))

predict(knnFit)
predict(knnFit, type = "prob")

bothModels <- list(knn = knnFit,
                   tree = rdaFit)

predict(bothModels)

extractPrediction(bothModels, testX = iris[1:10, -5])
extractProb(bothModels, testX = iris[1:10, -5])
  }

}
\references{
Kuhn (2008), ``Building Predictive Models in R Using the caret''
(\url{http://www.jstatsoft.org/article/view/v028i05/v28i05.pdf})
}
\seealso{
\code{\link{plotObsVsPred}}, \code{\link{plotClassProbs}},
\code{\link{trainControl}}
}
\author{
Max Kuhn
}
\keyword{manip}
