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Fitting and Testing Generalized Logistic Distributions in R

Overview

The R package glogis provides:

Density dglogis, distribution function pglogis, quantile function qglogis, and random generation rglogis for the type-I (skew-logistic) generalized logistic distribution.
Fitting univariate type-I generalized logistic distributions with location, scale, and shape parameters via glogisfit.
Interface to strucchange for fitting segmented type-I generalized logistic distributions to time series data.

Reference

Windberger T, Zeileis A (2014). “Structural Breaks in Inflation Dynamics within the European Monetary Union.” Eastern European Economics, 52(3), 66-88. doi:10.2753/EEE0012-8775520304

Installation

The stable version of glogis is available from CRAN:

install.packages("glogis")

The latest development version can be installed from R-universe:

install.packages("glogis", repos = "https://zeileis.R-universe.dev")

License

The package is available under the General Public License version 3 or version 2

Get started

Simulation of a simple artificial sample from a generalized logistic distribution.

library("glogis")
set.seed(2)
x <- rglogis(1000, location = -1, scale = 0.5, shape = 3)

Fitting the distribution via maximum likelihood.

gf <- glogisfit(x)
plot(gf)

summary(gf)
## 
## Call:
## glogisfit(x = x)
## 
## 
## Coefficients:
##            Estimate Std. Error z value Pr(>|z|)    
## location   -1.16961    0.18840  -6.208 5.36e-10 ***
## log(scale) -0.63017    0.04323 -14.578  < 2e-16 ***
## log(shape)  1.29581    0.25916   5.000 5.73e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Log-likelihood: -1074 on 12 Df
## Goodness-of-fit statistic: 39.11 on 58 DF,  p-value: 0.9731
## Number of iterations in BFGS optimization: 15

Querying parameters and associated moments.

coef(gf)
##   location log(scale) log(shape) 
## -1.1696110 -0.6301687  1.2958079
coef(gf, log = FALSE)
##   location      scale      shape 
## -1.1696110  0.5325019  3.6539469
gf$parameters
##   location      scale      shape 
## -1.1696110  0.5325019  3.6539469
gf$moments
##       mean   variance   skewness 
## -0.2483885  0.5556121  0.8407388

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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