
Predicts gene expressions using pretrained models.
predict(models, attribute, weather)
A list of trained models for the genes of interest.
At the moment the collection of trained models returned
by FIT::train()
cannot be directly passed to FIT::predict()
:
the user has to explicitly convert it to an appropriate format by using
FIT::train.to.predict.adaptor()
.
(This restriction might be removed in a future.)
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$Attribute,
but this may be subject to change.)
An object that represents actual or hypothetical weather data
with which predictions of gene expressions are made.
The object can be created from a dumped/saved dataframe
of size ntimepoints * nfactors
using FIT::load.weather()
.
(At the moment it is an instance of a hidden class IO$Weather,
but this may be subject to change.)
A list of prediction results as returned by the models.
# NOT RUN {
# prepare models
# NOTE: FIT::train() returns a nested list of models
# so we have to flatten it using FIT::train.to.predict.adaptor()
# before passing it to FIT::predict().
models <- FIT::train(..)
models.flattened <- FIT::train.to.predict.adaptor(models)
# load data used for prediction
prediction.attribute <- FIT::load.attribute('attribute.2009.txt')
prediction.weather <- FIT::load.weather('weather.2009.dat', 'weather')
prediction.expression <- FIT::load.expression('expression.2009.dat', 'ex', genes)
prediction.results <- FIT::predict(models.flattened,
prediction.attribute,
prediction.weather)
# }
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