# 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>
#   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:            DESCRIPTION:
#  nFit                 Fits parameters of a Normal density
################################################################################


nFit <- 
    function(x, doplot = TRUE, span = "auto", title = NULL, 
    description = NULL, ...)
{   
    # A function implemented by Diethelm Wuertz
    
    # Description:
    #   Return Maximum log-likelihood estimated
    #   Paramters for Normal Distribution
      
    # Notes:
    #   Function Calls: nlminb(), density() 
    #   The function normFit() can be found in the Rmetrics
    #       chapter GarchDistributions.
   
    # FUNCTION:
    
    # Transform:
    x.orig = x
    x = as.vector(x)
    
    # Settings:
    CALL = match.call()
    
    # MLE:
    N = length(x)
    mean = sum(x)/N
    sd = sqrt(sum((x-mean)^2)/N) 

    # Optional Plot:
    if (doplot) {
        if (span == "auto") {
            span.min = qnorm(0.001, mean, sd)
            span.max = qnorm(0.999, mean, sd)
            span = seq(span.min, span.max, length = 100)  
        }
        par(err = -1)
        z = density(x, n = 100, ...)
        x = z$x[z$y > 0]
        y = z$y[z$y > 0]
        y.points = dnorm(span, mean, sd)
        ylim = log(c(min(y.points), max(y.points)))
        plot(x, log(y), xlim = c(span[1], span[length(span)]),  
            ylim = ylim, type = "p", xlab = "x", ylab = "log f(x)", ...)
        title("NORMAL: Parameter Estimation")
        lines(x = span, y = log(y.points), col = "steelblue") 
        if (exists("grid")) grid()  
    }
    
    # Add Title and Description:
    if (is.null(title)) title = "Normal Parameter Estimation"
    if (is.null(description)) description = .description()
        
    # Fit:
    fit = list(
        estimate = c(mean = mean, sd = sd), 
        minimum = sum(log(dnorm(x, mean, sd))),
        code = NA)
        
    # Return Value:
    new("fDISTFIT",     
        call = as.call(CALL),
        model = "Normal Distribution",
        data = as.data.frame(x.orig),
        fit = fit,
        title = as.character(title), 
        description = .description() )
}


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