optim: A raw API for optimizing model parameters.
In FIT: Transcriptomic Dynamics Models in Field Conditions

View source: R/FIT.R View source: R/Train.R

Note: use train() unless the user is willing to accept breaking API changes in the future.

1 2	optim(expression, weight, attribute, weather, recipe, models, maxit = NULL, nfolds = NULL)

`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$Attribute, but this may be subject to change.)
`weight`	A matrix of size `nsamples * ngenes` that during regression penalizes errors from each sample using the formula `sum_{s in samples} (weight_s) (error_s)^2`. Note that, unlike for `FIT::train()`, this argument is NOT optional.
`attribute`	An object that represents the attributes of microarray/RNA-seq data. The object can be created from a dumped/saved dataframe of size `nsamples * nattributes` using `FIT::load.attribute()`. (At the moment it is an instance of a hidden class IO$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()`.
`models`	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.)
`maxit`	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()`.
`nfolds`	An optional number that specifies the order of cross validation when `optim` method is `'lasso'`. This is simply ignored when `optim` method is `'lm'`.