File: 1C-RollingAnalysis.R

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# 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:
#  rollFun                   Compute Rolling Function Value
#   rollMean                  Compute Rolling Mean
#   rollVar                   Compute Rolling Variance
#   rollMin                   Compute Rolling Minimum
#   rollMax                   Compute Rolling Maximum
################################################################################


rollFun = 
function(x, n, trim = TRUE, na.rm = FALSE, FUN, ...) 
{   # A function implemented by Diethelm Wuertz

    # Description:
    #   Compute rolling function value
    
    # Arguments:
    #   x - an univariate "timeSeries" object or a numeric vector.
    #   n - an integer specifying the number of periods or 
    #       terms to use in each rolling/moving sample.
    #   trim - a logical flag: if TRUE, the first n-1 missing values in 
    #       the returned object will be removed; if FALSE, they will 
    #       be saved in the returned object. The default is TRUE.
    #   na.rm - a logical flag: if TRUE, missing values in x will be  
    #       removed before computation. The default is FALSE.
    #   FUN - the rolling function, arguments to this function can be
    #       passed through the \code{\dots} argument.
    
    # FUNCTION:
    
    # Transform:
    x.orig = x
    if (is.timeSeries(x)) {
        stopifnot(isUnivariate(x))
        TS = TRUE 
    } else {
        TS = FALSE
    }
    if (TS) {
        positions = x.orig@positions
        x = x.orig@Data[, 1]
        
    } else {
        x = as.vector(x.orig)
        names(x) = NULL
    }
    
    # Remove NAs:
    if (na.rm) { 
        if (TS) positions = positions[!is.na(x)]
        x = as.vector(na.omit(x))
    }
    
    # Roll FUN:
    start = 1
    end = length(x)-n+1
    m = x[start:end]
    if (n > 1) {
        for (i in 2:n) {
            start = start + 1
            end = end + 1
            m = cbind(m, x[start:end])
        }
    } else {
        m = matrix(m)
    }
    
    # Result:
    ans = apply(m, MARGIN = 1, FUN = FUN, ...)
    
    # Trim:
    if (!trim) 
        ans = c(rep(NA, (n-1)), ans)
    if (trim & TS)
        positions = positions[-(1:(n-1))]
        
    # Back to timeSeries:
    if (TS) {
        ans = timeSeries(as.matrix(ans), positions, recordIDs = data.frame(),
            units = x.orig@units, FinCenter = x.orig@FinCenter)
    }
    
    # Return value:
    ans
}


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

rollMean = 
function(x, n = 9, trim = TRUE, na.rm = FALSE) 
{   # A function implemented by Diethelm Wuertz

    # Description:
    #   Compute rolling mean

    # Examples:
    #
    #   x = timeSeries(as.matrix(cumsum(rnorm(12))), timeCalendar(), 
    #       units = "rnorm",FinCenter = "GMT")
    #   rollMean(x, n = 4, trim = FALSE, na.rm = FALSE)
    #   rollMean(x, n = 4, trim = TRUE, na.rm = FALSE)
    #
    #   x@Data[8, ] = NA
    #   rollMean(x, n = 4, trim = FALSE, na.rm = FALSE)
    #   rollMean(x, n = 4, trim = FALSE, na.rm = TRUE)
    #   rollMean(x, n = 4, trim = TRUE, na.rm = TRUE)
    
    # FUNCTION:
    
    # Roll Mean:
    rmean = rollFun(x = x, n = n, trim = trim, na.rm = na.rm, FUN = mean) 
    
    # Return Value:
    rmean
}
    
    
# ------------------------------------------------------------------------------


rollVar  = 
function(x, n = 9, trim = TRUE, unbiased = TRUE, na.rm = FALSE) 
{   # A function implemented by Diethelm Wuertz

    # Description:
    #   Compute rolling variance

    # FUNCTION:
    
    # Handle Time Series:
    if (is.timeSeries(x)) TS = TRUE else TS = FALSE 
   
    # Roll Var:
    rvar = rollFun(x = x, n = n, trim = trim, na.rm = na.rm, FUN = var) 
    
    # Unbiased ?
    if (!unbiased) {
        if (TS) {
            rvar@Data = (rvar@Data * (n-1))/n
        } else {
            rvar = (rvar * (n-1))/n
        }
    }

    # Return Value:
    rvar 
}
    

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


rollMax  = 
function(x, n = 9, trim = TRUE, na.rm = FALSE) 
{   # A function implemented by Diethelm Wuertz

    # Description:
    #   Compute rolling maximum

    # FUNCTION:
    
    # Roll Max:
    rmax = rollFun(x = x, n = n, trim = trim, na.rm = na.rm,  FUN = max)
    
    # Return Value:
    rmax 
}
    

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


rollMin  = 
function(x, n = 9, trim = TRUE, na.rm = FALSE) 
{   # A function implemented by Diethelm Wuertz

    # Description:
    #   Compute rolling function minimum

    # FUNCTION:
 
    # Roll Min:
    rmin = rollFun(x = x, n = n, trim = trim, na.rm = na.rm,  FUN = min) 
    
    # Return Value:
    rmin 
}


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