# Documentation

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# lognlike

Lognormal negative log-likelihood

## Syntax

```nlogL = lognlike(params,data) [nlogL,avar] = lognlike(params,data) [...] = lognlike(params,data,censoring) [...] = lognlike(params,data,censoring,freq) ```

## Description

`nlogL = lognlike(params,data)` returns the negative log-likelihood of `data` for the lognormal distribution with parameters `params`. `params(1)` is the mean of the associated normal distribution, `mu`, and `params(2)` is the standard deviation of the associated normal distribution, `sigma`. The values of `mu` and `sigma` are scalars, and the output `nlogL` is a scalar.

`[nlogL,avar] = lognlike(params,data)` returns the inverse of Fisher's information matrix. If the input parameter value in `params` is the maximum likelihood estimate, `avar` is its asymptotic variance. `avar` is based on the observed Fisher's information, not the expected information.

`[...] = lognlike(params,data,censoring)` accepts a Boolean vector, `censoring`, of the same size as `data`, which is 1 for observations that are right-censored and 0 for observations that are observed exactly.

`[...] = lognlike(params,data,censoring,freq)` accepts a frequency vector, `freq`, of the same size as `data`. The vector `freq` typically contains integer frequencies for the corresponding elements in `data`, but can contain any nonnegative values. Pass in `[]` for `censoring` to use its default value.