glogis
Fitting and Testing Generalized Logistic Distributions
Tools for the generalized logistic distribution (Type I, also known as skew-logistic distribution), encompassing basic distribution functions (p, q, d, r, score), maximum likelihood estimation, and structural change methods.
Fitting and Testing Generalized Logistic Distributions in R
Overview
The R package glogis provides:
-
Density
dglogis, distribution functionpglogis, quantile functionqglogis, and random generationrglogisfor 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