reltest_predict: Make prediction from one model

Description

Make prediction from one model

Usage

reltest_predict(
  model,
  xx,
  tt,
  tt1,
  tt2,
  tt3,
  n0,
  n10,
  n20,
  n30,
  pp,
  params,
  dmgs = TRUE,
  debug = FALSE,
  aderivs = TRUE,
  unbiasedv = FALSE,
  pwm = FALSE,
  minxi = -10,
  maxxi = 10
)

Value

Two vectors

Arguments

model: which distribution to test. Possibles values are "exp", "pareto_k2", "halfnorm", "unif", "norm", "norm_dmgs", "gnorm_k3", "lnorm", "lnorm_dmgs", "logis", "lst_k3", "cauchy", "gumbel", "frechet_k1", "weibull", "gev_k3", "exp_p1", "pareto_p1k2", "norm_p1", "lnorm_p1", "logis_p1", "lst_p1k3", "cauchy_p1", "gumbel_p1", "frechet_p2k1", "weibull_p2", "exp_p1k4", "norm_p12", "lst_p12k3", "gamma", "invgamma", "invgauss", "gev", "gpd_k1", "gev_p1". "gev_p12". "gev_p123".
xx: training data
tt: predictor vector
tt1: predictor vector 1
tt2: predictor vector 2
tt3: predictor vector 3
n0: index for predictor vector
n10: index for predictor vector 1
n20: index for predictor vector 2
n30: index for predictor vector 2
pp: probabilites at which to make quantile predictions
params: model parameters
dmgs: flag for whether to run dmgs calculations or not
debug: flag for turning debug messages on
aderivs: a logical for whether to use analytic derivatives (instead of numerical)
unbiasedv: a logical for whether to use the unbiased variance instead of maxlik (for the normal)
pwm: a logical for whether to use PWM instead of maxlik (for the GEV)
minxi: minimum value for EVT shape parameter
maxxi: maximum value for EVT shape parameter