# mlebgumbel: Bimodal Gumbel: Maximum Likelihood Estimation In bgumbel: Bimodal Gumbel Distribution

## Description

Bimodal Gumbel: Maximum Likelihood Estimation

## Usage

 `1` ```mlebgumbel(data, theta, auto = TRUE) ```

## Arguments

 `data` A numeric vector. `theta` Vector. Starting parameter values for the minimization. Default: theta = c(1, 1, 1) `auto` Logical. Automatic search for theta initial condition. Default: TRUE

List.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41``` ```# Let's generate some values set.seed(123) x <- rbgumbel(1000, mu = -2, sigma = 1, delta = -1) # Look for these references in the figure: hist(x, probability = TRUE) lines(density(x), col = 'blue') abline(v = c(-2.5, -.5), col = 'red') text(x = c(c(-2.5, -.5)), y = c(.05, .05), c('mu\nnear here', 'delta\nnear here')) # Time to fit! # If argument auto = FALSE fit <- mlebgumbel( data = x, # try some values near the region. Format: theta = c(mu, sigma, delta) theta = c(-3, 2, -2), auto = FALSE ) print(fit) # If argument auto = TRUE fit <- mlebgumbel( data = x, auto = TRUE ) print(fit) # Kolmogorov-Smirnov Tests mu.sigma.delta <- fit\$estimate\$estimate ks.test( x, y = 'pbgumbel', mu = mu.sigma.delta[[1]], sigma = mu.sigma.delta[[2]], delta = mu.sigma.delta[[3]] ) ```

bgumbel documentation built on April 1, 2021, 1:06 a.m.