### Function Reference: `logllike`

statistics: nlogL = logllike (params, x)
statistics: [nlogL, acov] = logllike (params, x)
statistics: […] = logllike (params, x, censor)
statistics: […] = logllike (params, x, censor, freq)

Negative log-likelihood for the log-logistic distribution.

`nlogL = logllike (params, x)` returns the negative log likelihood of the data in x corresponding to the log-logistic distribution with (1) scale parameter a and (2) shape parameter b given in the two-element vector params.

`[nlogL, acov] = logllike (params, x)` also returns the inverse of Fisher’s information matrix, acov. If the input parameter values in params are the maximum likelihood estimates, the diagonal elements of params are their asymptotic variances.

`[…] = logllike (params, x, censor)` accepts a boolean vector, censor, of the same size as x with `1`s for observations that are right-censored and `0`s for observations that are observed exactly. By default, or if left empty, `censor = zeros (size (x))`.

`[…] = logllike (params, x, censor, freq)` accepts a frequency vector, freq, of the same size as x. freq typically contains integer frequencies for the corresponding elements in x, but it can contain any non-integer non-negative values. By default, or if left empty, `freq = ones (size (x))`.

Further information about the loglogistic distribution can be found at https://en.wikipedia.org/wiki/Log-logistic_distribution

OCTAVE/MATLAB use an alternative parameterization given by the pair μ, σ, i.e. mu and sigma, in analogy with the logistic distribution. Their relation to the α and b parameters used in Wikipedia are given below:

- `mu = log (a)`
- `sigma = 1 / a`

See also: loglcdf, loglinv, loglpdf, loglrnd, loglfit, loglstat

Source Code: logllike