Note: use train() unless the user is willing to
accept breaking API changes in the future.
init(expression, weight, attribute, weather, recipe)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$Attribute,
but this may be subject to change.)
A matrix of size nsamples * ngenes
that during regression penalizes errors from each sample
using the formula
sum_{s in samples} (weight_s) (error_s)^2.
Note that, unlike for FIT::train(), this argument
is NOT optional.
An object that represents the attributes of a
microarray/RNA-seq data.
The object can be created from a dumped/saved dataframe
of size nsamples * nattributes
using FIT::load.attribute().
(At the moment it is an instance of a hidden class IO$Attribute,
but this may be subject to change.)
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.)
An object that represents the training protocol of models.
A recipe can be created using FIT::make.recipe().
A collection of models whose parameters are
set by using the 'init' method in the argument recipe.