diffloglik (Model)
Approximate gradient and hessian of log-likelihood function
Syntax
[mll, Grad, Hess, varScale] = diffloglik(M, Inp, Range, PList, ...)
Input arguments
`M` [ model ]
Model object whose likelihood function will be differentiated
`Inp` [ cell | struct ]
Input data from which measurement variables will be taken.
`Range` [ numeric | char ]
Date range on which the likelihood function will be evaluated.
`PList` [ cellstr ]
List of model parameters with respect to which the likelihood function will be differentiated.
Output arguments
`mll` [ numeric ]
Value of minus the likelihood function at the input data.
`Grad` [ numeric ]
Gradient (or score) vector.
`Hess` [ numeric ]
Hessian (or information) matrix.
`varScale` [ numeric ]
Estimated variance scale factor if the
'relative='options is true; otherwisevis 1.
Options
`'CheckSteady='` [ `true` | *`false`* | cell ]
Check steady state in each iteration; works only in non-linear models.
`'Solve='` [ *`true`* | `false` | cellstr ]
Re-compute solution for each parameter change; you can specify a cell array with options for the
solvefunction.
`'Sstate='` [ `true` | *`false`* | cell ]
Re-compute steady state in each differentiation step; if the model is non-linear, you can pass in a cell array with options used in the
sstate( )function.See help on
model/filterfor other options available.