Description Usage Arguments Details Value References See Also Examples
Fit a univariate generalized logisitc distribution (Type I: skewlogistic with location, scale, and shape parameters) to a sample of observations.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18  glogisfit(x, ...)
## Default S3 method:
glogisfit(x, weights = NULL, start = NULL, fixed = c(NA, NA, NA),
method = "BFGS", hessian = TRUE, ...)
## S3 method for class 'formula'
glogisfit(formula, data, subset, na.action, weights, x = TRUE, ...)
## S3 method for class 'glogisfit'
plot(x, main = "", xlab = NULL, fill = "lightgray",
col = "blue", lwd = 1, lty = 1, xlim = NULL, ylim = NULL,
legend = "topright", moments = FALSE, ...)
## S3 method for class 'glogisfit'
summary(object, log = TRUE, breaks = NULL, ...)
## S3 method for class 'glogisfit'
coef(object, log = TRUE, ...)
## S3 method for class 'glogisfit'
vcov(object, log = TRUE, ...)

x 
a vector of observation (may be a 
weights 
optional numeric vector of weights. 
start 
optional vector of starting values. The parametrization has to be
in terms of 
fixed 
specification of fixed parameters (see description of 
method 
character string specifying optimization method, see 
hessian 
logical. Should the Hessian be used to compute the variance/covariance
matrix? If 
formula 
symbolic description of the model, currently only 
data, subset, na.action 
arguments controlling formula processing
via 
main, xlab, fill, col, lwd, lty, xlim, ylim 
standard graphical parameters, see

legend 
logical or character specification where to place a legend.

moments 
logical. If a legend is produced, it can either show the parameter
estimates ( 
object 
a fitted 
log 
logical option in some extractor methods indicating whether scale and shape parameters should be reported in logs (default) or the original levels. 
breaks 
interval breaks for the chisquared goodnessoffit test. Either a numeric vector of two or more cutpoints or a single number (greater than or equal to 2) giving the number of intervals. 
... 
arguments passed to methods. 
glogisfit
estimates the generalized logistic distribution (Type I: skewlogistic)
as given by dglogis
. Optimization is performed numerically by
optim
using analytical gradients. For obtaining numerically more
stable results the scale and shape parameters are specified in logs. Starting values
are chosen as c(0, 0, 0)
, i.e., corresponding to a standard (symmetric) logistic
distribution. If these fail, better starting values are obtained by running a NelderMead
optimization on the original problem (without logs) first.
A large list of standard extractor methods is supplied to conveniently compute
with the fitted objects, including methods to the generic functions
print
, summary
, plot
(reusing hist
and lines
), coef
,
vcov
, logLik
, residuals
,
and estfun
and
bread
(from the sandwich package).
The methods for coef
, vcov
, summary
, and bread
report computations
pertaining to the scale/shape parameters in logs by default, but allow for switching back to
the original levels (employing the delta method).
Visualization employs a histogramm of the original data along with lines for the estimated density.
Further structural change methods for "glogisfit"
objects are described in
breakpoints.glogisfit
.
glogisfit
returns an object of class "glogisfit"
, i.e., a list with components as follows.
coefficients 
estimated parameters from the model (with scale/shape in logs, if included), 
vcov 
associated estimated covariance matrix, 
loglik 
loglikelihood of the fitted model, 
df 
number of estimated parameters, 
n 
number of observations, 
nobs 
number of observations with nonzero weights, 
weights 
the weights used (if any), 
optim 
output from the 
method 
the method argument passed to the 
parameters 
the full set of model parameters (location/scale/shape), including estimated and fixed parameters, all in original levels (without logs), 
moments 
associated mean/variance/skewness, 
start 
the starting values for the parameters passed to the 
fixed 
the original specification of fixed parameters, 
call 
the original function call, 
x 
the original data, 
converged 
logical indicating successful convergence of 
terms 
the terms objects for the model (if the 
Shao Q (2002). Maximum Likelihood Estimation for Generalised Logistic Distributions. Communications in Statistics – Theory and Methods, 31(10), 1687–1700.
Windberger T, Zeileis A (2014). Structural Breaks in Inflation Dynamics within the European Monetary Union. Eastern European Economics, 52(3), 66–88.
dglogis
, dlogis
, breakpoints.glogisfit
1 2 3 4 5 6 7 8 9 10 11 12 
Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, as.Date.numeric
Call:
glogisfit(x = x)
Coefficients:
Estimate Std. Error z value Pr(>z)
location 1.16961 0.18840 6.208 5.36e10 ***
log(scale) 0.63017 0.04323 14.578 < 2e16 ***
log(shape) 1.29581 0.25916 5.000 5.73e07 ***

Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Loglikelihood: 1074 on 12 Df
Goodnessoffit statistic: 39.11 on 58 DF, pvalue: 0.9731
Number of iterations in BFGS optimization: 15
location log(scale) log(shape)
1.1696110 0.6301687 1.2958079
location scale shape
1.1696110 0.5325019 3.6539469
location scale shape
1.1696110 0.5325019 3.6539469
mean variance skewness
0.2483885 0.5556121 0.8407388
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