# 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 - 2006, Diethelm Wuertz, 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:            DESCRIPTION:    
#  'fDISTFIT'           S4 Class representation
#  print.fDISTFIT       Prints Results from a Fitted Distribution
# FUNCTION:            NORMAL AND T DISTRIBUTION:
#  nFit                 Fits parameters of a Normal density
#  tFit                 Fits parameters of a Student-t density
# FUNCTION:            STABLE DISTRIBUTION:
#  stableFit            Fits parameters of a stable density
# FUNCTION:            GENERALIZED DISTRIBUTION:
#  ghFit                Fits parameters of a generalized hyperbolic density
#  hypFit               Fits parameters of a hyperbolic density
#  nigFit               Fits parameters of a normal inverse Gaussian density
################################################################################


test.nFit = 
function()
{ 
    # Graph Frame:
    par(mfrow = c(1, 1))
     
    # Simulate normal random variates:
    RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion")
    set.seed(4711, kind = "Marsaglia-Multicarry")
    s = rnorm(n = 2000, mean = 1, sd = 0.5) 
    
    # Fit:
    ans = nFit(x = s)
    
    # Precision within 10% ?
    relErrorTest =  c(
        ( (ans@fit$estimate[1] - 1.0)/1.0 < 0.10 ), 
        ( (ans@fit$estimate[2] - 0.5)/0.5 < 0.10 ))
    print(ans)
    print(relErrorTest)
    checkTrue(as.logical(mean(relErrorTest)))

    # Return Value:
    return()    
}


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


test.tFit = 
function()
{ 
    # Graph Frame:
    par(mfrow = c(1, 1))
    
    # Simulate random variates t(4):
    RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion")
    set.seed(4711, kind = "Marsaglia-Multicarry")
    s = rt(n = 2000, df = 4)
    
    # Fit:  
    ans = tFit(x = s, df = 2*var(s)/(var(s)-1), trace = FALSE)
    
    # Precision of df within 10% ?
    relErrorTest =  c(
        ( (ans@fit$estimate[1] - 4.0)/4.0 < 0.10 ))
    print(ans)
    print(relErrorTest)
    checkTrue(as.logical(mean(relErrorTest)))
    
    # Return Value:
    return()    
}


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


test.ghFit = 
function()
{ 
    # Graph Frame:
    par(mfrow = c(1, 1))
    
    # Simulate random variates:
    RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion")
    set.seed(4711, kind = "Marsaglia-Multicarry")
    s = rgh(n = 2000, alpha = 0.8, beta = 0.2, delta = 2, mu = -0.4, 
        lambda = 1) 
    
    # Fit:
    ans = ghFit(x = s, alpha = 1, beta = 0, delta = 1, mu = 0, lambda = 1,
        trace = FALSE) 
    
    # Precision of parameters within 10% ?
    relErrorTest =  c(
        ( (ans@fit$estimate[1] - 0.8)/( 0.8) < 0.10 ), 
        ( (ans@fit$estimate[2] - 0.2)/( 0.2) < 0.10 ),
        ( (ans@fit$estimate[3] - 2.0)/( 2.0) < 0.10 ),
        ( (ans@fit$estimate[4] + 0.4)/(-0.4) < 0.10 ),
        ( (ans@fit$estimate[5] - 2.0)/( 2.0) < 0.10 ))
    print(ans)
    print(relErrorTest)
    checkTrue(as.logical(mean(relErrorTest)))
    
    # Return Value:
    return()    
}


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


test.hypFit = 
function()
{ 
    # Graph Frame:
    par(mfrow = c(1, 1))
    
    # Simulate normal random variates:
    RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion")
    set.seed(4711, kind = "Marsaglia-Multicarry")
    s = rhyp(n = 2000, alpha = 1.5, beta = 0.8, delta = 0.5, mu = -1) 
    
    # Fit:
    ans = hypFit(s, alpha = 1, beta = 0, delta = 1, mu = mean(s), 
        trace = FALSE)
    
    # Precision of parameters within 10% ?
    relErrorTest =  c(
        ( (ans@fit$estimate[1] - 1.5)/( 1.5) < 0.10 ), 
        ( (ans@fit$estimate[2] - 0.8)/( 0.8) < 0.10 ),
        ( (ans@fit$estimate[3] - 0.5)/( 0.5) < 0.10 ),
        ( (ans@fit$estimate[4] + 1.0)/(-1.0) < 0.10 ))
    print(ans)
    print(relErrorTest)
    checkTrue(as.logical(mean(relErrorTest)))
    
    # Return Value:
    return()    
}


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


test.nigFit = 
function()
{ 
    # Graph Frame:
    par(mfrow = c(1, 1))
    
    # Simulate normal random variates:
    RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion")
    set.seed(4711, kind = "Marsaglia-Multicarry")
    s = rnig(n = 2000, alpha = 1.5, beta = -0.7, delta = 0.5, mu = -1.0) 
    
    # Fit:
    ans = nigFit(s, alpha = 1, beta = 0, delta = 1, mu = mean(s), 
        trace = FALSE)
    
    # Precision of parameters within 10% ?
    relErrorTest =  c(
        ( (ans@fit$estimate[1] - 1.5)/( 1.5) < 0.10 ), 
        ( (ans@fit$estimate[2] + 0.7)/(-0.7) < 0.10 ),
        ( (ans@fit$estimate[3] - 0.5)/( 0.5) < 0.10 ),
        ( (ans@fit$estimate[4] + 1.0)/(-1.0) < 0.10 ))
    print(ans)
    print(relErrorTest)
    checkTrue(as.logical(mean(relErrorTest)))
    
    # Return Value:
    return()
}


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


test.stableFit = 
function()
{   
    # Graph Frame:
    par(mfrow = c(1, 1))
    
    # Simulate stable random variates:
    RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion")
    set.seed(4711, kind = "Marsaglia-Multicarry")
    s = rstable(500, alpha=1.8, beta=0.3, gamma = 1, delta = 0.1, pm = 0) 
    
    # Fit:
    ans = stableFit(x = s, alpha = 1.5) 
    print(ans)  # CHECK: call
    
    # Precision of parameters within 10% ?
    relErrorTest =  c(
        ( (ans@fit$estimate[1] - 1.8)/( 1.8) < 0.10 ), 
        ( (ans@fit$estimate[2] - 0.3)/( 0.3) < 0.10 ),
        ( (ans@fit$estimate[3] - 1.0)/( 1.0) < 0.10 ),
        ( (ans@fit$estimate[4] - 0.1)/( 0.1) < 0.10 ))
    print(ans)
    print(relErrorTest)
    checkTrue(as.logical(mean(relErrorTest)))
    
    # MLE Fit:
    if (FALSE) {
        # Note, this takes rather long time ...
        ans = stableFit(x = s, alpha = 1.5, type = "mle", trace = TRUE) 
        # .mleStableFit(s, 1.75, 0, 1, 0)
        # The result would be:
        #
        # Precision of parameters within 10% ?
        relErrorTest =  c(
            ( (ans@fit$estimate[1] - 1.8)/( 1.8) < 0.10 ), 
            ( (ans@fit$estimate[2] - 0.3)/( 0.3) < 0.10 ),
            ( (ans@fit$estimate[3] - 1.0)/( 1.0) < 0.10 ),
            ( (ans@fit$estimate[4] - 0.1)/( 0.1) < 0.10 ))      
        print(ans)
        print(relErrorTest)
        checkTrue(as.logical(mean(relErrorTest)))
    }
    
    # Return Value:
    return() 
}
 
   
################################################################################