train: Constructs models following a recipe.
In FIT: Transcriptomic Dynamics Models in Field Conditions

Description Usage Arguments Value Examples

Constructs models following a recipe.

1 2	train(expression, attribute, weather, recipe, weight = NULL, min.expressed.rate = 0.01)

`expression`	An object that represents gene expression data. The object can be created from a dumped/saved dataframe of size `nsamples * ngenes` using `FIT::load.expression()`. (At the moment it is an instance of a hidden class IO$Expression, but this may be subject to change.)
`attribute`	An object that represents the attributes of microarray/RNA-seq data. The object can be created from a dumped/saved dataframe of size `nsamples * nattributes` using `FIT::load.attribute()`. (At the moment it is an instance of a hidden class IO$Attribute, but this may be subject to change.)
`weather`	An object that represents actual or hypothetical weather data with which the training of models are done. The object can be created from a dumped/saved dataframe of size `ntimepoints * nfactors` using `FIT::load.weather()`. (At the moment it is an instance of a hidden class IO$Weather, but this may be subject to change.)
`recipe`	An object that represents the training protocol of models. A recipe can be created using `FIT::make.recipe()`.
`weight`	An optional numerical matrix of size `nsamples * ngenes` that during regression penalizes errors from each sample using the formula `sum_{s in samples} (weight_s) (error_s)^2`. This argument is optional for a historical reason, and when it is omitted, all samples are equally penalized.
`min.expressed.rate`	A number used to A gene with `var(expr) < thres.expr` is regarded as unexpressed, and FIT sets its model as: `expr = log(offset) + 0*inputs`.

A collection of trained models.

## Not run: 
# create recipe
recipe <- FIT::make.recipe(..)

#load training data
training.attribute  <- FIT::load.attribute('attribute.2008.txt');
training.weather    <- FIT::load.weather('weather.2008.dat', 'weather')
training.expression <- FIT::load.expression('expression.2008.dat', 'ex', genes)
training.weight     <- FIT::load.weight('weight.2008.dat', 'weight', genes)

# train models
models <- FIT::train(training.expression,
                     training.attribute,
                     training.weather,
                     recipe,
                     training.weight)

## End(Not run)