Estimation of the bivariate angular dependence function of Wadsworth and Tawn (2013)
Source:R/multivar.R
      lambdadep.RdEstimation of the bivariate angular dependence function of Wadsworth and Tawn (2013)
Usage
lambdadep(
  dat,
  qu = 0.95,
  method = c("hill", "mle", "bayes"),
  plot = TRUE,
  level = 0.95,
  ...
)Arguments
- dat
- an \(n\) by \(2\) matrix of multivariate observations 
- qu
- quantile level on uniform scale at which to threshold data. Default to 0.95 
- method
- string indicating the estimation method 
- plot
- logical indicating whether to return the graph of - lambda
- level
- level for confidence intervals, default to 0.95 
- ...
- additional arguments, used for backward compatibility - The confidence intervals are based on normal quantiles. The standard errors for the - hillare based on the asymptotic covariance and that of the- mlederived using the delta-method. Bayesian posterior predictive interval estimates are obtained using ratio-of-uniform sampling with flat priors: the shape parameters are constrained to lie within the triangle, as are frequentist point estimates which are adjusted post-inference.
Value
a plot of the lambda function if plot=TRUE, plus an invisible list with components
- wthe sequence of angles in (0,1) at which the- lambdavalues are evaluated
- lambdapoint estimates of lambda
- lower.confint- level% confidence interval for lambda (lower bound)
- upper.confint- level% confidence interval for lambda (upper bound)
Examples
set.seed(12)
dat <- mev::rmev(n = 1000, d = 2, model = "log", param = 0.1)
lambdadep(dat, method = 'hill')
#> Error in lambdadep(dat, method = "hill"): object 'ci_level' not found
if (FALSE) { # \dontrun{
lambdadep(dat, method = 'bayes')
lambdadep(dat, method = 'mle')
# With independent observations
dat <- matrix(runif(n = 2000), ncol = 2)
lambdadep(dat, method = 'hill')
} # }