laplafit: Fit a Laplace Distribution via maximum likelihood

In Rsubbotools: Fast Estimation of Subbottin and AEP Distributions (Generalized Error Distribution)

Fit a Laplace Distribution via maximum likelihood

Description

laplafit returns the parameters, standard errors. negative log-likelihood and covariance matrix of the Laplace Distribution for a sample. See details below.

Usage

laplafit(data, verb = 0L, interv_step = 10L, provided_m_ = NULL)

Arguments

data	(NumericVector) - the sample used to fit the distribution.
verb	(int) - the level of verbosity. Select one of: 0 just the final result 1 details of optim. routine
interv_step	int - the number of intervals to be explored after the last minimum was found in the interval optimization. Default is 10.
provided_m_	NumericVector - if NULL, the m parameter is estimated by the routine. If numeric, the estimation fixes m to the given value.

Details

The Laplace distribution is a distribution controlled by two parameters, with formula:

f(x;a,m) = \frac{1}{2a} e^{- \left| \frac{x-m}{a} \right| }

where a is a scale parameter, and m is a location parameter. The estimations are produced by maximum likelihood, where analytical formulas are available. Details on the method can be found on the package vignette.

Value

a list containing the following items:

• "dt" - dataset containing parameters estimations and standard deviations.

• "log-likelihood" - negative log-likelihood value.

• "matrix" - the covariance matrix for the parameters.

Examples

sample_subbo <- rpower(1000, 1, 1)
laplafit(sample_subbo)

Rsubbotools documentation built on April 16, 2025, 5:10 p.m.