prediction.errors: Computes the prediction errors using the trained models.

In FIT: Transcriptomic Dynamics Models in Field Conditions

Description

Computes the prediction errors using the trained models.

Usage

prediction.errors(models, expression, attribute, weather)

Arguments

models	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.)
expression	An object that represents the actual measured data of gene expressions. 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$Attribute, but this may be subject to change.)
attribute	An object that represents the attributes of microarray/RNA-seq data. The object can be created from a dumped/saved dataframe using FIT::load.attribute(). (At the moment it is an instance of a hidden class IO$Attribute, but this may be subject to change.)
weather	An object that represents actual or hypothetical weather data with which predictions of gene expressions are made. The object can be created from a dumped/saved dataframe using FIT::load.weather(). (At the moment it is an instance of a hidden class IO$Weather, but this may be subject to change.)

Value

A list of deviance (a measure of validity of predictions, as is defined by each model) between the prediction results and the measured results (as is provided by the user through expression argument).

Examples

## Not run: 
# see the usage of FIT::predict()

## End(Not run)

FIT documentation built on May 2, 2019, 3:38 p.m.