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#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 of the License, or
# (at your option) any later version.
#
# This program 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 General Public License for more details.
#
# A copy of the GNU General Public License is available at
# ../../COPYING
################################################################################
# FUNCTION: DESCRIPTION:
# statsColnames Returns statistically rearranged column names
# orderColnames Returns ordered column names of a time Series
# sortColnames Returns sorted column names of a time Series
# sampleColnames Returns sampled column names of a time Series
# pcaColnames Returns PCA correlation ordered column names
# hclustColnames Returns hierarchical clustered column names
################################################################################
statsColnames =
function(x, FUN = colMeans, ...)
{
# A function implemented by Diethelm Wuertz
# Description:
# Returns statistically rearranged column names
# Arguments:
# x - an object of class timeSeries
# FUN - a character string, the name of the function to be used
# ... - optional arguments to be passed to FUN
# Note:
# Example of function Candidates:
# colStats calculates column statistics,
# colSums calculates column sums,
# colMeans calculates column means,
# colSds calculates column standard deviations,
# colVars calculates column variances,
# colSkewness calculates column skewness,
# colKurtosis calculates column kurtosis,
# colMaxs calculates maximum values in each column,
# colMins calculates minimum values in each column,
# colProds computes product of all values in each column,
# colQuantiles computes quantiles of each column.
# FUNCTION:
# Apply colStats Function:
fun = match.fun(FUN)
Sort = sort(fun(x, ...))
Order = names(Sort)
ans = colnames(as.matrix(x)[, Order])
attr(ans, "control") <- Sort
# Return Value:
ans
}
# ------------------------------------------------------------------------------
orderColnames =
function(x, ...)
{
# A function implemented by Diethelm Wuertz
# Description:
# Returns ordered column names of a time Series
# Arguments:
# x - an object of class timeSeries
# FUNCTION:
# Order:
ans = order(colnames(as.matrix(x)), ...)
# Return Value:
ans
}
# ------------------------------------------------------------------------------
sortColnames =
function(x, ...)
{
# A function implemented by Diethelm Wuertz
# Description:
# Returns sorted column names of a time Series
# Arguments:
# x - an object of class timeSeries
# FUNCTION:
# Sort:
ans = sort(colnames(as.matrix(x)), ...)
# Return Value:
ans
}
# ------------------------------------------------------------------------------
sampleColnames =
function(x, ...)
{
# A function implemented by Diethelm Wuertz
# Description:
# Returns sampled column names of a time Series
# Arguments:
# x - an object of class timeSeries
# FUNCTION:
# Sample:
ans = sample(colnames(as.matrix(x)), ...)
# Return Value:
ans
}
# ------------------------------------------------------------------------------
pcaColnames =
function(x, robust = FALSE, ...)
{
# A function implemented by Diethelm Wuertz
# Description:
# Returns PCA correlation ordered column names
# Arguments:
# x - an object of class timeSeries
# robust - a logical, should we use robust covariance estimation?
# FUNCTION:
# Order:
if (robust) {
if (require(robustbase))
x.cor = covMcd(as.matrix(x), cor = TRUE, ...)$cor
else
stop("package \"robustbase\" cannot be loaded")
} else {
x.cor = cor(as.matrix(x), ...)
}
x.eigen = eigen(x.cor)$vectors[,1:2]
e1 = x.eigen[, 1]
e2 = x.eigen[, 2]
Order = order(ifelse(e1 > 0, atan(e2/e1), atan(e2/e1)+pi))
ans = colnames(as.matrix(x))[Order]
# Return Value:
ans
}
# ------------------------------------------------------------------------------
hclustColnames =
function(x, method = c("euclidean", "complete"), ...)
{
# A function implemented by Diethelm Wuertz
# Description:
# Returns hierarchical clustered column names
# Arguments:
# x - an object of class timeSeries
# method - the agglomeration method to be used. This should
# be (an unambiguous abbreviation of) one of "ward", "single",
# "complete", "average", "mcquitty", "median" or "centroid".
# ... optional arguments passed to the function hclust
# FUNCTION:
# Order:
Order = hclust(dist(t(as.matrix(x)),
method = method[1]), method = method[2], ...)$order
ans = colnames(as.matrix(x))[Order]
# Return Value:
ans
}
################################################################################
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