laplafit {Rsubbotools} | R Documentation |
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:
|
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)