\name{trainControl}
\alias{trainControl}
\title{Control parameters for train}
\description{Control the computational nuances of the \code{\link{train}} function}
\usage{
trainControl(method = "boot", 
             number = ifelse(grepl("cv", method), 10, 25),
             repeats = ifelse(grepl("cv", method), 1, number),
             p = 0.75, 
             initialWindow = NULL,
             horizon = 1,
             fixedWindow = TRUE,
             verboseIter = FALSE,
             returnData = TRUE,
             returnResamp = "final",
             savePredictions = FALSE,
             classProbs = FALSE,
             summaryFunction = defaultSummary,
             selectionFunction = "best",
             preProcOptions = list(thresh = 0.95, ICAcomp = 3, k = 5),
             index = NULL,
             indexOut = NULL,
             timingSamps = 0,
             predictionBounds = rep(FALSE, 2),
             seeds = NA,
             adaptive = list(min = 5, alpha = 0.05, 
             	               method = "gls", complete = TRUE),
             trim = FALSE,
             allowParallel = TRUE)
}
\arguments{
  \item{method}{The resampling method: \code{boot}, \code{boot632}, \code{cv}, \code{repeatedcv},
    \code{LOOCV},  \code{LGOCV} (for repeated training/test splits), \code{none} (only fits one model to the entire training set), 
    \code{oob} (only for random forest, bagged trees, bagged earth, bagged flexible discriminant analysis, or conditional tree forest models), \code{"adaptive_cv"}, \code{"adaptive_boot"} or \code{"adaptive_LGOCV"}}
  \item{number}{Either the number of folds or number of resampling iterations}
  \item{repeats}{For repeated k-fold cross-validation only: the number of complete sets of folds to compute}
  \item{verboseIter}{A logical for printing a training log.}
  \item{returnData}{A logical for saving the data}
  \item{returnResamp}{A character string indicating how much of the resampled summary metrics should be saved. Values can be ``final'', ``all'' or ``none''}
  \item{savePredictions}{a logical to save the hold-out predictions for each resample}
  \item{p}{For leave-group out cross-validation: the training percentage}
  \item{initialWindow, horizon, fixedWindow}{possible arguments to \code{\link{createTimeSlices}}}
  \item{classProbs}{a logical; should class probabilities be computed for classification models (along with predicted values) in each resample?}
  \item{summaryFunction}{a function to compute performance metrics across resamples. The arguments to the function should be the same as those in \code{\link{defaultSummary}}.}
  \item{selectionFunction}{the function used to select the optimal tuning parameter. This can be a name of the function or the function itself. See \code{\link{best}} for details and other options.}
  \item{preProcOptions}{A list of options to pass to \code{\link{preProcess}}. The type of pre-processing (e.g. center, scaling etc) is passed in via the \code{preProc} option in \code{\link{train}}.}
  \item{index}{a list with elements for each resampling iteration. Each list element is the sample rows used for training at that iteration.}
  \item{indexOut}{a list (the same length as \code{index}) that dictates which sample are held-out for each resample. If \code{NULL}, then the unique set of samples not contained in \code{index} is used.}
  \item{timingSamps}{the number of training set samples that will be used to measure the time for predicting samples (zero indicates that the prediction time should not be estimated.}
  \item{predictionBounds}{a logical or numeric vector of length 2 (regression only). If logical, the predictions can be constrained to be within the limit of the training set outcomes. For example, a value of \code{c(TRUE, FALSE)} would only constrain the lower end of predictions. If numeric, specific bounds can be used. For example, if \code{c(10, NA)}, values below 10 would be predicted as 10 (with no constraint in the upper side).}
  \item{seeds}{an optional set of integers that will be used to set the seed at each resampling iteration. This is useful when the models are run in parallel. A value of \code{NA} will stop the seed from being set within the worker processes while a value of \code{NULL} will set the seeds using a random set of integers. Alternatively, a list can be used. The list should have \code{B+1} elements where \code{B} is the number of resamples. The first \code{B} elements of the list should be vectors of integers of length \code{M} where \code{M} is the number of models being evaluated. The last element of the list only needs to be a single integer (for the final model). See the Examples section below and the Details section. }
  \item{adaptive}{a list used when \code{method} is \code{"adaptive_cv"}, \code{"adaptive_boot"} or \code{"adaptive_LGOCV"}. See Details below. }
  \item{trim}{a logical. If \code{TRUE} the final model in \code{object\$finalModel} may have some components of the object removed so reduce the size of the saved object. The \code{predict} method will still work, but some other features of the model may not work. \code{trim}ing will occur only for models where this feature has been implemented. }
  \item{allowParallel}{if a parallel backend is loaded and available, should the function use it?}
}

\value{
 An echo of the parameters specified
}

\details{
When setting the seeds manually, the number of models being evaluated is required. This may not be obvious as \code{train} does some optimizations for certain models. For example, when tuning over PLS model, the only model that is fit is the one with the largest number of components. So if the model is being tuned over \code{comp in 1:10}, the only model fit is \code{ncomp = 10}. However, if the vector of integers used in the \code{seeds} arguments is longer than actually needed, no error is thrown. 

Using \code{method = "none"} and specifying more than one model in \code{\link{train}}'s \code{tuneGrid} or \code{tuneLength} arguments will result in an error. 

Using adaptive resampling when \code{method} is either \code{"adaptive_cv"}, \code{"adaptive_boot"} or \code{"adaptive_LGOCV"}, the full set of resamples is not run for each model. As resampling continues, a futility analysis is conducted and models with a low probability of being optimal are removed. These features are experimental. See Kuhn (2014) for more details. The options for this procedure are:

\itemize{
 \item \code{min}: the minimum number of resamples used before models are removed
 \item \code{alpha}: the confidence level of the one-sided intervals used to measure futility
 \item \code{method}: either generalized least squares (\code{method = "gls"}) or a Bradley-Terry model (\code{method = "BT"})
  \item \code{complete}: if a single parameter value is found before the end of resampling, should the full set of resamples be computed for that parameter. )
}
}

\author{Max Kuhn}

\references{Kuhn (2014), ``Futility Analysis in the Cross-Validation of Machine Learning Models'' \url{http://arxiv.org/abs/1405.6974}}

\examples{
\dontrun{

## Do 5 repeats of 10-Fold CV for the iris data. We will fit
## a KNN model that evaluates 12 values of k and set the seed
## at each iteration.

set.seed(123)
seeds <- vector(mode = "list", length = 51)
for(i in 1:50) seeds[[i]] <- sample.int(1000, 22)

## For the last model:
seeds[[51]] <- sample.int(1000, 1)

ctrl <- trainControl(method = "repeatedcv", 
                     repeats = 5,
                     seeds = seeds)

set.seed(1)
mod <- train(Species ~ ., data = iris, 
             method = "knn", 
             tuneLength = 12,
             trControl = ctrl)


ctrl2 <- trainControl(method = "adaptive_cv", 
                      repeats = 5,
                      verboseIter = TRUE,
                      seeds = seeds)

set.seed(1)
mod2 <- train(Species ~ ., data = iris, 
              method = "knn", 
              tuneLength = 12,
              trControl = ctrl2)

}
}

\keyword{utilities}
