Note: use train()
unless the user is willing to
accept breaking API changes in the future.
optim(expression, weight, attribute, weather, recipe, models, maxit = NULL,
nfolds = NULL)
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$Attribute,
but this may be subject to change.)
A matrix of size nsamples * ngenes
that during regression penalizes errors from each sample
using the formula
sum_{s in samples} (weight_s) (error_s)^2
.
Note that, unlike for FIT::train()
, this argument
is NOT optional.
An object that represents the attributes of
microarray/RNA-seq data.
The object can be created from a dumped/saved dataframe
of size nsamples * nattributes
using FIT::load.attribute()
.
(At the moment it is an instance of a hidden class IO$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 being trained as is returnd by
FIT::init()
.
At this moment, it must be a list (genes) of a list (envs) of models and must contain at least one model. (THIS MIGHT CHANGE IN A FUTURE.)
An optional number that specifies the maximal number of times that the parameter optimization is performed.
The user can control this parameter by using the opts
argument
for FIT::train()
.
An optional number that specifies the order of
cross validation when optim
method is 'lasso'
.
This is simply ignored when optim
method is 'lm'
.
A collection of models whose parameters are
optimized by using the 'optim'
pipeline
in the argument recipe
.