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.
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 0.010419817To get the current released version from CRAN:
install.packages("fRegression")