[MinusLogLik,Grad,Hess,V] = diffloglik(M,Inp,Range,PList,...)
M
[ model ] - Model object whose likelihood function will be differentiated.
Inp
[ cell | struct ] - Input data from which measurement variables will be taken.
Range
[ numeric ] - 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.
MinusLogLik
[ numeric ] - Value of minus the likelihood function at the input data.
Grad
[ numeric ] - Gradient (or score) vector.
Hess
[ numeric ] - Hessian (or information) matrix.
V
[ numeric ] - Estimated variance scale factor if the 'relative='
options is true; otherwise v
is 1.
'chkSstate='
[ true
| false
| cell ] - Check steady state in each iteration; works only in non-linear models.
'refresh='
[ true
| false
] - Refresh dynamic links for each change in a parameter.
'solve='
[ true
| false
| cellstr ] - Re-compute solution for each parameter change; you can specify a cell array with options for the solve
function.
'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/filter
for other options available.