00fRegression-package: Regression Modelling Package

Description Details 1 Introduction 2 Fitting Regression Models 2 Simulation of Regression Models 3 Extractor Functions 4 Forecasting 4 Reporting Functions About Rmetrics:


The Rmetrics "fRegression" package is a collection of functions for linear and non-linear regression modelling.


Package: fRegression
Type: Package
Version: R 3.0.1
Date: 2014
License: GPL Version 2 or later
Copyright: (c) 1999-2014 Rmetrics Association
Repository: R-FORGE
URL: https://www.rmetrics.org

1 Introduction

Regression modelling, especially linear modelling, LM, is a widely used application in financial engineering. In finance it mostly appears in form that a variable is modelled as a linear or more complex relationship as a function of other variables. For example the decision of buying or selling in a trading model may be triggered by the outcome of a regression model, e.g. neural networks are a well known tool in this field.

2 Fitting Regression Models

Rmetrics has build a unique interface to several regression models available in the base and contributed packages of R. The following regression models are interfaced and available through a common function regFit. The argument use allows to select the desired model:

    regFit          fits regression models
    - lm            fits a linear model [stats]
    - rlm           fits a LM by robust regression [MASS]
    - glm           fits a generliazed linear model [stats]
    - gam           fits a generlized additive model [mgcv]
    - ppr           fits a projection pursuit regression model [stats]
    - nnet          fits a single hidden-layer neural network model [nnet]
    - polymars      fits an adaptive polynomial spline regression [polspline]

An advantage of the regFit function is, that all the underlying functions of its family can be called with the same list of arguments, and the value returned is always an unique object, an object of class "fREG" with the following slots: @call, @formula, @method, @data, @fit, @residuals, @fitted, @title, and @description.

Furthermore, independent of the selected regression model applied we can use the same S4 methods for all types of regressions. This includes, print,plot, summary, predict, fitted, residuals, coef, vcov, and formula methods.

It is possible to add further regression models to this framework either his own implementations or implementations available through other contributed R packages. Suggestions include biglm, earth amongst others.

2 Simulation of Regression Models

contains a function to simulate artificial regression models, mostly used for testing.

    regSim          simulates artificial regression model data sets

3 Extractor Functions

These generic functions are:

    fitted          extracts fitted values from a fitted 'fREG' object
    residuals       extracts residuals from a fitted 'fREG' object
    coef            extracts coefficients from a fitted 'fREG' object
    formula         extracts formula expression from a fitted 'fREG' object
    vcov            extracts variance-covariance matrix of fitted parameters

4 Forecasting

The function predict returns predicted values based on the fitted model object.

    predict         forecasts from an object of class 'fREG'

4 Reporting Functions

For printing and plotting use the functions:

    print           prints the results from a regression fit
    plot            plots the results from a gression fit
    summary         returns a summary report       

About Rmetrics:

The fRegression Rmetrics package is written for educational support in teaching "Computational Finance and Financial Engineering" and licensed under the GPL.

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