inst/unitTests/runit.snorm.R

In fGarch: Rmetrics - Autoregressive Conditional Heteroskedastic Modelling

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# 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:              SKEW NORMAL DISTRIBUTION:
#  dsnorm                 Density for the skew normal Distribution
#  psnorm                 Probability function for the skew NORM
#  qsnorm                 Quantile function for the skew NORM
#  rsnorm                 Random Number Generator for the skew NORM
# FUNCTION:              PARAMETER ESTIMATION:
#  snormFit               Fit the parameters for a skew Normal distribution
# FUNCTION:              SLIDER:
#  snormSlider           Displays Normal Distribution and RVS
################################################################################


test.snormDist <- 
    function()
{   
    # Normal Distribution:
    RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion")
    set.seed(4711, kind = "Marsaglia-Multicarry")
    
    # Test:
    test = fBasics::distCheck("norm",  mean = 0, sd = 1, robust = FALSE)
    print(test)
    checkTrue(sum(test) == 3)                                     
    
    # Skew Normal Distribution:
    RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion")
    set.seed(4711, kind = "Marsaglia-Multicarry")
    
    # Test:
    test = fBasics::distCheck("snorm", mean = 0, sd = 1, xi = 1.5, robust = FALSE) 
    print(test)
    checkTrue(sum(test) == 3)                                      
    
    # Return Value:
    return()    
}


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


test.snormFit <- 
    function()
{      
    # Parameter Estimation:
    #  snormFit - Fit the parameters for a skew Normal distribution
    
    # Skew Normal Distribution:
    RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion")
    set.seed(4711, kind = "Marsaglia-Multicarry")
    
    # Series:
    x = rsnorm(n = 1000, mean = 0, sd = 1, xi = 1.5)
    
    # Fit:
    fit = snormFit(x)
    print(fit)
    
    # Return Value:
    return()    
}


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


test.snormSlider <- 
    function()
{   
    # Try Distribution:
    # snormSlider(type = "dist")
    NA
   
    # Try Random Variates:
    RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion")
    set.seed(4711, kind = "Marsaglia-Multicarry")
    
    # snormSlider(type = "rand")
    NA 
    
    # Return Value:
    return()    
}


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