% Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/resample_class.R
 \name{resample}
 \alias{resample}
 \title{Data resampling}
 \usage{
 resample(
   number_of_iterations = 10,
   method = "split_data",
   factor_name,
   p_train = 0.8,
   collect = NULL,
   ...
 )
 }
 \arguments{

 \item{number_of_iterations}{(numeric, integer) The number of training sets to generate. The default is \code{10}.\cr}

 \item{method}{(character) Resampling method. Allowed values are limited to the following: \itemize{ \item{\code{"split_data"}: Samples for the training set are selected at random from the full dataset.}\item{\code{"stratified_split"}: Samples for the training set are randomly selected from each level of the chosen factor.}\item{\code{"equal_split"}: Samples for the training set are selected at random from each level of the main factor such that all group sizes are equal.}} The default is \code{"split_data"}.}

 
 \item{factor_name}{(character) The name of a sample-meta column to use.}
 

 \item{p_train}{(numeric) The proportion of samples selected for the training set. The default is \code{0.8}.\cr}

 
 \item{collect}{(NULL, character) The name of a model output to collect over all bootstrap repetitions, in addition to the input metric. The default is \code{NULL}.}
 
 \item{...}{Additional slots and values passed to \code{struct_class}.}
 }
 \value{
 A  \code{resample} object with the following \code{output} slots:
 \tabular{ll}{
 \code{results.training} \tab          (data.frame)  \cr
 \code{results.testing} \tab          (data.frame)  \cr
 \code{metric} \tab          (data.frame)  \cr
 \code{collected} \tab          (list)  \cr
 \code{metric.train} \tab          (numeric)  \cr
 \code{metric.test} \tab          (numeric)  \cr
 }
 }
 \description{
 New training sets are generated from the original data by selecting samples at random. This can be based on levels in a factor or on the whole dataset.
 }

 \section{Inheritance}{
 
 A \code{resample} object inherits the following \code{struct} classes: \cr\cr

 \verb{[resample]} >> \verb{[resampler]} >> \verb{[iterator]} >> \verb{[struct_class]}

 }
 

 \examples{

 M = resample(
       number_of_iterations = 100,
       method = "split_data",
       factor_name = "V1",
       p_train = 0.75,
       collect = NULL)
 

 I = resample(
     number_of_iterations = 10, 
     factor_name = 'Species', 
     method = 'split_data',
     p_train = 0.8)
 }