fun.minmax.check.gld

This function checks the lowest and highest quantiles of the specified GLDs 
against the 
specified dataset

distribution

The fitting algorithms considered in this package have two major objectives. One is to provide a smoothing device to fit distributions to data using the weight and unweighted discretised approach based on the bin width of the histogram. The other is to provide a definitive fit to the data set using the maximum likelihood and quantile matching estimation. Other methods such as moment matching, starship method, L moment matching are also provided. Diagnostics on goodness of fit can be done via qqplots, KS-resample tests and comparing mean, variance, skewness and kurtosis of the data with the fitted distribution. References include the following: Karvanen and Nuutinen (2008) "Characterizing the generalized lambda distribution by L-moments" <doi:10.1016/j.csda.2007.06.021>, King and MacGillivray (1999) "A starship method for fitting the generalised lambda distributions" <doi:10.1111/1467-842X.00089>, Su (2005) "A Discretized Approach to Flexibly Fit Generalized Lambda Distributions to Data" <doi:10.22237/jmasm/1130803560>, Su (2007) "Nmerical Maximum Log Likelihood Estimation for Generalized Lambda Distributions" <doi:10.1016/j.csda.2006.06.008>, Su (2007) "Fitting Single and Mixture of Generalized Lambda Distributions to Data via Discretized and Maximum Likelihood Methods: GLDEX in R" <doi:10.18637/jss.v021.i09>, Su (2009) "Confidence Intervals for Quantiles Using Generalized Lambda Distributions" <doi:10.1016/j.csda.2009.02.014>, Su (2010) "Chapter 14: Fitting GLDs and Mixture of GLDs to Data using Quantile Matching Method" <doi:10.1201/b10159>, Su (2010) "Chapter 15: Fitting GLD to data using GLDEX 1.0.4 in R" <doi:10.1201/b10159>, Su (2015) "Flexible Parametric Quantile Regression Model" <doi:10.1007/s11222-014-9457-1>, Su (2021) "Flexible parametric accelerated failure time model"<doi:10.1080/10543406.2021.1934854>.

Steve Su

GLDEX

Fitting Single and Mixture of Generalised Lambda Distributions

Martin Maechler

Juha Karvanen

Robert King

Benjamin Dean

R Core Team

fun.minmax.check.gld function

<dl> <dt>data</dt>
<dd>A vector of numerical dataset</dd> <dt>lambdas</dt>
<dd>A matrix of four columns representing lambda 1 to lambda 4 of the GLD</dd> <dt>param</dt>
<dd>Can be "rs", "fkml" or "fmkl"</dd> <dt>lessequalmin</dt>
<dd>Can be 0 or 1</dd> <dt>greaterequalmax</dt>
<dd>Can be 0 or 1</dd></dl>

Arguments

Author

Check whether the specified GLDs cover the minimum and the maximum values in a 
dataset — fun.minmax.check.gld

<dl>

 <dt>data</dt>
<dd>A vector of numerical dataset</dd>

 <dt>lambdas</dt>
<dd>A matrix of four columns representing lambda 1 to lambda 4 of the GLD</dd>

 <dt>param</dt>
<dd>Can be "rs", "fkml" or "fmkl"</dd>

 <dt>lessequalmin</dt>
<dd>Can be 0 or 1</dd>

 <dt>greaterequalmax</dt>
<dd>Can be 0 or 1</dd>

</dl>

fun.minmax.check.gld: Check whether the specified GLDs cover the minimum and the maximum values in a dataset

Description

Usage

Value

Arguments

Author

Details

Examples