# This library is free software; you can redistribute it and/or
# modify it under the terms of the GNU Library General Public
# License as published by the Free Software Foundation; either
# version 2 of the License, or (at your option) any later version.
#
# This library is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Library General Public License for more details.
#
# You should have received a copy of the GNU Library General
# Public License along with this library; if not, write to the
# Free Foundation, Inc., 59 Temple Place, Suite 330, Boston,
# MA  02111-1307  USA

# Copyrights (C)
# for this R-port: 
#   1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL
#   Diethelm Wuertz <wuertz@itp.phys.ethz.ch>
#   info@rmetrics.org
#   www.rmetrics.org
# for the code accessed (or partly included) from other R-ports:
#   see R's copyright and license files
# for the code accessed (or partly included) from contributed R-ports
# and other sources
#   see Rmetrics's copyright file


################################################################################
# FUNCTION:             PARAMETER ESTIMATION:
#  regFit                Wrapper Function for Regression Models
#  .lmFit                 Linear Regression Model
#  .rlmFit                Robust Linear Regression Model
#  .glmFit                Generalized Linear Model
#  .gamFit                Generalized Additive Model
#  .pprFit                Projection Pursuit Regression Model
#  .nnetFit               Feedforward Neural Network Model
#  .polymarsFit           Polytochomous MARS Model
################################################################################


################################################################################
# MODEL:        PACKAGE     print   plot   summary   print     predict
#                                    persp           summary
#   lm          stats       x       x      x         x         x
#   rlm         MASS
#   glm         stats       x       -      x         x         x
#   gam         mgcv        x       x      x         x         x
#   ppr         modreg      x       x      x         x         x
#   nnet        nnet        x       -      x         x         x
#   polymars*   polspline   -       xx     x         -         x
################################################################################


regFit <- 
    function (formula, data, family = gaussian, 
    use = c("lm", "rlm", "glm", "gam", "ppr", "nnet", "polymars"), 
    title = NULL, description = NULL, ...) 
{   
    # A function implemented by Diethelm Wuertz

    # Description:
    #   Common function call for several selected regression models.
    
    # Details:
    #   This is a wrapper function for the following regrssion models:
    #     LM          Linear Regression Modelling
    #     RLM         Robust Linear Regression Modelling
    #     GLM         Generalized Linear Modelling
    #     GAM         Generalized Additive Modelling
    #     PPR         Projection Pursuit Regression
    #     NNET        Feedforward Neural Net
    #     POLYMARS    Polytochomous MARS Modeling
    
    # Notes:
    #   Available Methods are
    #   "print", "plot", "summary", "predict"
    #   "coef", "formula", "residuals" "fitted", "vcov" 
    
    # Example:
    #   regFit(Y ~ X1 + X2 + X3, regSim())
    
    # FUNCTION:
    
    # Match Arguments:
    use = match.arg(use)
   
    # Transform data into a dataframe
    Data = data
    data = as.data.frame(data)
    
    # Function to be called:
    fun = paste(".", match.arg(use), sep = "")

    # Title:
    if (is.null(title)) {
        if (use == "lm") title = "Linear Regression Modeling"
        if (use == "rlm") title = "Robust Linear Regression Modeling"
        if (use == "glm") title = "Generalized Linear Modeling"
        if (use == "gam") title = "Generalized Additive Modeling"
        if (use == "ppr") title = "Projection Pursuit Regression"
        if (use == "nnet") title = "Feedforward Neural Network Modeling" 
        if (use == "polymars") title = "Polytochomous MARS Modeling"
    } 
    
    # Description:
    if (is.null(description)) {
        description = .description()
    }
    
    # Compose Command to be Called:
    cmd = match.call()
    if (!is.null(cmd$use)) cmd = cmd[-match("use", names(cmd), 0)]    
    cmd[[1]] <- as.name(fun)
    
    # Fit Regression Model:
    fit <- eval(cmd, parent.frame()) 
      
    # Add "cmd" to Fit:
    fit$cmd = cmd
    
    # Add "xlevels" to Fit (if missing):
    if (is.null(fit$xlevels)) fit$xlevels = list()
    
    # Add "residuals" and "fitted" to Fit (to be sure ...):
    fit$residuals = as.vector(fit$residuals)    
    fit$fitted.values = as.vector(fit$fitted.values)
    
    # Add "parameters" as Alternative:
    fit$parameters = fit$coef
 
    # Extend to class "list":
    class(fit) = c("list", class(fit))
    if (!inherits(fit, "lm")) class(fit) = c(class(fit), "lm")

    # Return Value:
    new("fREG",     
        call = as.call(match.call()),
        formula = as.formula(formula), 
        family = as.character(gaussian()),
        method = use,
        # data is data.frame, Data original input:
        data = list(data = data, Data = Data),
        fit = fit,
        residuals = fit$residuals,
        fitted = fit$fitted.values,
        title = as.character(title), 
        description = as.character(description)
    )
}


# ------------------------------------------------------------------------------


.lm <- stats::lm
.rlm <- MASS::rlm
.glm <- stats::glm
.gam <- mgcv::gam
.ppr <- function(..., nterms = 2) 
    stats::ppr(..., nterms = nterms)
.nnet <- function(..., trace = FALSE, size = 2, linout = TRUE) 
    nnet::nnet(..., trace = trace, size = size, linout = linout)
.polymars <- .polymarsFormula


################################################################################