Calculate Hessian matrix given parameter values and data
calc_hessian(
pars_opt,
pars_const,
observations,
modelfun,
dose_norm,
log10_trans
)A square numeric matrix, both dimensions the same as the length of `pars_opt`. It will have rownames and column names that are the same as the names of `pars_opt`.
Named numeric: A vector of parameter values for the parameters that were optimized. For example, you can get this using [coef.pk()] with `include_type = "optim"`.
Named numeric: A vector of parameter values for parameters that were held constant, not optimized (but are necessary to evaluate the model). For example, you can get this using [coef.pk()] with `include_type = "const"`.
The data used to fit the model. For example, you can get this using [get_data.pk()].
The name of the function that evaluates the model (passed to [log_likelihood()]).
Logical: Whether to dose-normalize concentrations before evaluating log-likelihood. Passed to [log_likelihood()].
Logical: Whether to log10-transform concentrations before evaluating log-likelihood. Passed to [log_likelihood()].
Caroline Ring
Calculate the Hessian matrix: the matrix of second derivatives of the objective function with respect to parameters, evaluated for a single set of parameter values for a single model and a single data set. Here, the objective function is the negative log-likelihood implemented in [log_likelihood()], evaluated jointly across the data that was used to fit the model. This is a workhorse function called by [get_hessian.pk()] and, indirectly, by [coef_sd.pk()]. When the number of optimized parameters is \(n\), the respective Hessian matrix will be \(n \times n\).