calc_hessian {invivoPKfit} | R Documentation |
Calculate Hessian
Description
Calculate Hessian matrix given parameter values and data
Usage
calc_hessian(
pars_opt,
pars_const,
observations,
modelfun,
dose_norm,
log10_trans
)
Arguments
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"'. |
pars_const |
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"'. |
observations |
The data used to fit the model. For example, you can get this using [get_data.pk()]. |
modelfun |
The name of the function that evaluates the model (passed to [log_likelihood()]). |
dose_norm |
Logical: Whether to dose-normalize concentrations before evaluating log-likelihood. Passed to [log_likelihood()]. |
log10_trans |
Logical: Whether to log10-transform concentrations before evaluating log-likelihood. Passed to [log_likelihood()]. |
Details
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
.
Value
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'.
Author(s)
Caroline Ring