ms_flat_1tail: Illustration of Model Selection Among 10 One Tail...

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

Illustration of Model Selection Among 10 One Tail Distributions from the fitdistcp Package

Description

Applies model selection using AIC, WAIC1, WAIC2 and leave-one-out logscore to the input data x, for 10 one tailed models in the fitdistcp package (although for the GPD, the logscore is NA for mathematical reasons).

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

The input data may be automatically shifted so that the minimum value is positive.

For the Pareto, the data may be further shifted so that the minimum value is slightly greater than 1.

Usage

ms_flat_1tail(x)

Arguments

x	data vector

Details

The 10 models are: exp, pareto_k2, halfnorm, lnorm, frechet_k1, weibull, gamma, invgamma, invgauss and gpd_k1.

Value

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

• MLE parameter values

• 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=rlnorm(nx)
print(ms_flat_1tail(x))

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