fmvcpot: Pairwise composite likelihood estimation for...

In lbelzile/ExtLiouv: Functions for 'Extremal attractor of Liouville copulas'

Description

This function finds the maximum composite likelihood estimate for multivariate extreme value models based on pairs of data. It returns a list with the result from optim along with some of the information passed to the function.

Usage

fmvcpot(dat, u, lambda, N, model = c("log", "neglog", "ct", "dir", "negdir",
  "hr", "xstud"), cscale = FALSE, cshape = FALSE, sym = FALSE,
  start = NULL, std.err = TRUE, method = "BFGS", warn.inf = TRUE, ...)

Arguments

dat	n by d data matrix on original scale
u	d vector of threshold parameters on probability scale
lambda	vector of percentage of threshold exceedances (can be omitted)
N	total number of observations if some observations falling under all threshold were removed from x (can be omitted)
model	family of multivariate extreme value distribution. 'ct' and 'hr' are the only family currently supported.
cscale	logical indicating common scale for all variables
cshape	logical indicating common shape for all variables
sym	logical indicating whether equal dependence parameters in the Dirichlet model
start	vector of starting value for the optimization
std.err	logical indicating whether to calculate standard errors numerically
method	optimization method; see optim
warn.inf	whether to check the validity of starting values before optimization
...	additional arguments (fixed parameters) passed to the optim function to be held fixed during the optimization

Value

a list containing the output of optim, notably parameter values par and standard errors se

Warning

users should strip the data matrix from full NA cases or else provide N. While these will be ignored in the routine, they are taken into account in the calculation of the total number of non exceedances.

Author(s)

Leo Belzile, some Cpp code by Adrien de Casto and Leonid Rousniak for ‘hr’ and ‘xstud’ models

Examples

set.seed(4)
x <- mev::rmev(n=10000, d=2, param=c(1,2,0.5), model = "negdir")
qu <- apply(x, 2, quantile,probs <- 0.99)
y <- x[which(rowSums(isAbove(x,threshold = qu))>=1),]
fit <- fmvcpot(dat=y, u=qu, model="negdir",lambda=colSums(isAbove(y,qu))/(nrow(x)+1), N=nrow(x),
               cscale=TRUE, cshape=TRUE, shape=1, scale=100)
fit$par
x <- mev::rmev(n=10000, d=2, param=c(1,2,0.5), model = "dir")
qu <- apply(x, 2, quantile,probs <- 0.99)
y <- x[which(rowSums(isAbove(x,threshold = qu))>=1),]
fit <- fmvcpot(dat=y, u=qu, model="dir",lambda=colSums(isAbove(y,qu))/(nrow(x)+1), N=nrow(x),
               cscale=TRUE, cshape=TRUE, shape=1, scale=100)
fit$par

lbelzile/ExtLiouv documentation built on May 20, 2019, 8:28 p.m.