regressoR.learn: Learn a Regression Model from the Given Data Set

In thomasWeise/regressoR: An R Package for Fitting Models to 2-Dimensional (x-y) Data

Description

Use the learnerSelectoR package to apply a set of learners to a set of data representations and pick the approach which generalizes best.

The data is represented by two vectors, x and y.

Each learner must be function with exactly four arguments named metric, transformation.x, transformation.y, and metric.transformed Its parameter metric will be an instance of RegressionQualityMetric which guides the search on the actual, raw data. However, since we internally use the Transformation.applyDefault2D method from the dataTransformeR package by default to generate different representations of the raw data, each model fitting procedure may take place in two steps, first on a transformed representation of the data (metric.transformed based on transformation.x and transformation.y) and then the actual finalization fitting the actual metric.

A learner returns an instance of FittedModel which represents, well, the model it has fitted to its input data. Each learner thus represents the process of adapting a specific model to some data.

By default, this method uses all the learners which are generated by regressoR.defaultLearners.

The metricGenerator is a function which accepts two vectors x and y and returns an instance of RegressionQualityMetric. It will be used to generate the quality metrics for guiding the model fitters. Since we internally use the learning.learn method from the learnerSelectoR package, the model may be chosen based on cross-validation and the metric generator is then also used to generate quality metrics for the training and test datasets used internally. If nothing else is specified, we use RegressionQualityMetric.default to generate the quality metrics.

representations is a list of TransformedData2D instances providing alernative views on the data, or NULL if only the raw data should be concerned. By default, we use Transformation.applyDefault2D to get a set of representations if nothing else is specified.

The return value of this method will be an instance of FittedModel or NULL if no learner could produce any result.

Usage

regressoR.learn(x, y, learners = regressoR.defaultLearners(),
  representations = Transformation.applyDefault2D(x = x, y = y, addIdentity =
  TRUE), metricGenerator = RegressionQualityMetric.default, q = 0.75)

Arguments

x	the x coordinates, i.e., the input values
y	the y coordinates, i.e., the output values
learners	the learners to apply
representations	the list of data representations, or NULL if fitting should take place only on the raw data
metricGenerator	the metric generator function
q	the effort parameter: 0=minimum effort=fast/low quality, 1=maximum effort=slow=highest quality

Value

an instance of FittedModel which represents the relationship between the x and y values

Examples

## Not run: 
dx <- rnorm(100);
dy <- rnorm(n=100, mean=50*dx*dx-33);
plot(dx, dy)
result <- regressoR.learn(x=dx, y=dy);
result@f
# function (x)
# -32.5442186855071 + (x * (0.776119279549966 + (x * 49.7907873618706)))
result@quality
# [1] 0.2455075
dx.sorted <- sort(dx)
lines(dx.sorted, result@f(dx.sorted), col="red")

## End(Not run)

thomasWeise/regressoR documentation built on May 9, 2019, 8:12 p.m.