reltest_simulate: Random training data from one model

In fitdistcp: Distribution Fitting with Calibrating Priors for Commonly Used Distributions

Random training data from one model

Description

Random training data from one model

Usage

reltest_simulate(
  model = "exp",
  nx = 20,
  tt,
  tt1,
  tt2,
  tt3,
  params,
  minxi = -10,
  maxxi = -10
)

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", "gev_p1k3", "norm_p12", "lst_p12k3", "gamma", "invgamma", "invgauss", "gev", "gpd_k1", "gev_p1". "gev_p12". "gev_p123".
nx	the length of the training data to use.
tt	predictor vector
tt1	predictor vector 1
tt2	predictor vector 2
tt3	predictor vector 2
params	values for the parameters for the specified distribution
minxi	minimum value for EVT shape parameter
maxxi	maximum value for EVT shape parameter

Value

Vector

fitdistcp documentation built on June 8, 2025, 1:04 p.m.