# R/vineOrder.R In vines: Multivariate Dependence Modeling with Vines

#### Documented in vineOrder

```# vines: Multivariate Dependence Modeling with Vines
# Copyright (C) 2011-2015 Yasser Gonzalez Fernandez
# Copyright (C) 2011-2015 Marta Soto Ortiz
#
# This program is free software; you can redistribute it and/or modify
# 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.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.

vineOrderGreedy <- function (type, data, according = "kendall") {
n <- ncol(data)

if (according %in% c("pearson", "kendall", "spearman")) {
# Calculate the value of the given measure of association between
# each pair of variables and couple the variables with the largest
# absolute values.
weights <- 1 - abs(cor(data, method = according))
} else if (according %in% c("df")) {
# Fit bivariate t copulas to each pair of variables and couple
# the variables with the smaller degrees of freedom.
weights <- matrix(0, n, n)
for (currentRoot in seq(length = n)) {
for (j in seq(length = max(currentRoot - 1, 0))) {
x <- data[ , currentRoot]
y <- data[ , j]
copula <- tCopula(0)
rho <- calibKendallsTau(copula, cor(x, y, method = "kendall"))
eps <- .Machine\$double.eps^0.5
rho <- max(min(rho, 1 - eps), -1 + eps)
L <- function (df) loglikCopula(c(rho, df), cbind(x, y), copula)
df <- optimize(L, c(1, 30), maximum = TRUE)\$maximum
weights[currentRoot, j] <- df
weights[j, currentRoot] <- df
}
}
} else {
stop("invalid value ", dQuote(according), " for the according argument")
}

# Couple the pairs with the minimum values in the values matrix.

if (identical(type, "DVine")) {
tsp <- insert_dummy(as.TSP(weights), label = "dummy")
tour <- solve_TSP(tsp, method = "cheapest_insertion")
order <- cut_tour(tour, "dummy")
names(order) <- NULL
} else if (identical(type, "CVine")) {
root <- which.min(colSums(weights))
order <- c(root, seq(to = n)[-root])
}

order
}

vineOrderRandom <- function (type, data) {
n <- ncol(data)
sample(n, n)
}

vineOrder <- function (type, data, method = "greedy", ...) {
if (type %in% c("CVine", "DVine") && identical(method, "greedy")) {
vineOrderGreedy(type, data, ...)
} else if (identical(method, "random")) {
vineOrderRandom(type, data)
} else {
stop("invalid ordering method ", dQuote(method),
" for ", dQuote(type))
}
}
```

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vines documentation built on May 29, 2017, 6:53 p.m.