Description
Rmetrics - Regression Based Decision and Prediction.
Description
A collection of functions for linear and non-linear regression modelling. It implements a wrapper for several regression models available in the base and contributed packages of R.
README.md
fRegression
Rmetrics - Modelling Extreme Events in Finance
The fRegression package is a collection of functions for linear and non-linear regression modelling. It implements a wrapper for several regression models available in the base and contributed packages of R.
An example
The following code simulates some regression data and fits various models to these data.
library(fRegression)
# Simulate data: the response is linearly related to 3 explanatory variables
x <- regSim(model = "LM3", n = 100)
# Linear modelling
regFit(Y ~ X1 + X2 + X3, data = x, use = "lm")
#>
#> Title:
#> Linear Regression Modeling
#>
#> Formula:
#> Y ~ X1 + X2 + X3
#>
#> Family:
#> gaussian identity
#>
#> Model Parameters:
#> (Intercept) X1 X2 X3
#> 0.01578 0.73967 0.25128 -0.50611
# Robust linear modelling
regFit(Y ~ X1 + X2 + X3, data = x, use = "rlm")
#>
#> Title:
#> Robust Linear Regression Modeling
#>
#> Formula:
#> Y ~ X1 + X2 + X3
#>
#> Family:
#> gaussian identity
#>
#> Model Parameters:
#> (Intercept) X1 X2 X3
#> 0.01968 0.74264 0.24736 -0.50123
# Generalised additive modelling
regFit(Y ~ X1 + X2 + X3, data = x, use = "gam")
#>
#> Title:
#> Generalized Additive Modeling
#>
#> Formula:
#> Y ~ X1 + X2 + X3
#>
#> Family:
#> gaussian identity
#>
#> Model Parameters:
#> (Intercept) X1 X2 X3
#> 0.01578 0.73967 0.25128 -0.50611
# Projection pursuit modelling
regFit(Y ~ X1 + X2 + X3, data = x, use = "ppr")
#>
#> Title:
#> Projection Pursuit Regression
#>
#> Formula:
#> Y ~ X1 + X2 + X3
#>
#> Family:
#> gaussian identity
#>
#> Model Parameters:
#> -- Projection Direction Vectors --
#> term 1 term 2
#> X1 0.7950116 -0.4422500
#> X2 0.2733278 -0.4863312
#> X3 -0.5415242 -0.7535894
#> -- Coefficients of Ridge Terms --
#> term 1 term 2
#> 0.9163087 0.0439332
# Feed-forward neural network modelling
regFit(Y ~ X1 + X2 + X3, data = x, use = "nnet")
#>
#> Title:
#> Feedforward Neural Network Modeling
#>
#> Formula:
#> Y ~ X1 + X2 + X3
#>
#> Family:
#> gaussian identity
#>
#> Model Parameters:
#> a 3-2-1 network with 11 weights
#> options were - linear output units
#> [1] 3.3664690 0.5597762 0.2646774 -0.5300914 0.8276914 -0.4493467
#> [7] -0.1400424 0.2787105 -0.5420174 5.4429808 -6.7838054
# Polychotonous Multivariate Adaptive Regression Splines
regFit(Y ~ X1 + X2 + X3, data = x, use = "polymars")
#> 1 2 3 4 5 6
#> 0.9145273 1.1607611 1.0482997 -0.5673597 -0.4692621 -1.3336450
#> X1 X2 X3
#> 1 1.8197351 -0.39077723 0.24075985
#> 2 1.3704395 0.39665330 -0.02049151
#> 3 1.1963182 0.78156956 0.29685497
#> 4 -0.4068792 -0.01912605 0.55061347
#> 5 -0.6109788 -1.94431293 -0.71396821
#> 6 -1.5089120 -0.24550669 0.38003407
#>
#> Title:
#> Polytochomous MARS Modeling
#>
#> Formula:
#> Y ~ X1 + X2 + X3
#>
#> Family:
#> gaussian identity
#>
#> Model Parameters:
#> pred1 knot1 pred2 knot2 coefs SE
#> 1 0 NA 0 NA 0.01577838 0.009803798
#> 2 1 NA 0 NA 0.73967249 0.009930477
#> 3 3 NA 0 NA -0.50611270 0.010729997
#> 4 2 NA 0 NA 0.25127670 0.010419817
Installation
To get the current released version from CRAN:
install.packages("fRegression")