ms_flat_2tail: Illustration of Model Selection Among 18 Distributions from...

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

Illustration of Model Selection Among 18 Distributions from the fitdistcp Package

Description

Applies model selection using AIC, WAIC1, WAIC2 and leave-one-out logscore to the input data x, for 7 two tailed models in the fitdistcp packages

The code is straightforward, and the point is to illustrate what is possible using the model selection outputs from the fitdistcp routines.

Usage

ms_flat_2tail(x)

Arguments

x	data vector

Details

The 7 models are: norm, gnorm_k3, gumbel, logis, lst_k3, cauchy, gev

Value

Plots QQ plots to the screen, for each of the models, and returns a data frame containing

• AIC scores (times -0.5), AIC weights

• WAIC1 scores, WAIC1 weights

• WAIC2 scores, WAIC2 weights

• logscores, logscore weights

• maximum likelihood and calibrating prior means

• maximum likelihood and calibrating prior standard deviations

Author(s)

Stephen Jewson [e-mail]

Examples

 # because it's too slow for CRAN
set.seed(1)
nx=50
x=rnorm(nx)
print(ms_flat_2tail(x))

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