Description Usage Arguments Details Value Author(s) See Also Examples

Calculate an approximation to the hessian of the **negative**
log likelihood of a db or beta binomial distribution via a numerical (finite
differencing based) procedure as effected by optimHess().

1 |

`object` |
An object of class |

`x` |
Numeric vector of non-negative integer data, presumably the
data set on the basis of which |

`silent` |
Logical scalar. If the call to |

It is up to the user to make sure that (when `object`

is of class `"mleBb"`

) `object`

and `x`

are
“mutually compatible”, i.e. are appropriately paired up.

Note that this function calculates the hessian of the
**negative** log likelihood of the distribution in question,
as *minimised* by `optim()`

. Hence its inverse
is an estimate of the covariance matrix of the parameter estimates.
(Do *not* take the negative of this hessian before inverting
it to get the desired covariance matrix!)

This function is mainly present to investigate possible differences
between the numerical approximation to the hessian, which is what
`optim()`

uses in its maximisation procedure, and the analytic
form of the hessian.

A two-by-two positive definite (with any luck!) numeric matrix. Its inverse is an estimate of the covariance matrix of the parameter estimates.

Rolf Turner r.turner@auckland.ac.nz

`aHess()`

`mleDb()`

`mleBb()`

`optim()`

`optimHess()`

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