gevcdn: Rebuilding of the function from 'GEVcdn' package
In proto4426/PissoortThesis: Thesis in Extreme Value Theory

Description Usage Arguments Details Value References

this rebuilded version allows us to : 1. Better understand what is going on and 2. Speed-up function by e.g. reduce unuseful loops or remove printing methods

gevcdn.fit2(x, y, iter.max = 1000, n.hidden = 2, Th = gevcdn.logistic,
  fixed = NULL, init.range = c(-0.25, 0.25),
  scale.min = .Machine$double.eps, beta.p = 3.3, beta.q = 2,
  sd.norm = Inf, n.trials = 5, method = c("BFGS", "Nelder-Mead"),
  max.fails = 100, silent = F, ...)

gevcdn.bag2(x, y, iter.max = 1000, iter.step = 10, n.bootstrap = 30,
  n.hidden = 3, Th = gevcdn.logistic, fixed = NULL,
  init.range = c(-0.25, 0.25), scale.min = .Machine$double.eps,
  beta.p = 3.3, beta.q = 2, sd.norm = Inf, method = c("BFGS",
  "Nelder-Mead"), max.fails = 100, silent = TRUE, ...)

hyp.tan(x)

`x`	covariate matrix with number of rows equal to the number of samples and number of columns equal to the number of variables.
`y`	column matrix of target values with number of rows equal7 to the # of samples
`sd.norm`	Weight penalty regularization : sd parameter for normal distribution prior for the magnitude of input-hiddenlayer weights; equivalent to weight penalty regularization.#'

See other function's details in the GEVcdn package

A personalized ggplot2 theme object to add to every builded plots.

Cannon, A.J., 2010. A flexible nonlinear modelling framework for nonstationary generalized extreme value analysis in hydroclimatology. Hydrological Processes, 24: 673-685. DOI: 10.1002/hyp.7506

proto4426/PissoortThesis documentation built on May 26, 2019, 10:31 a.m.