get_hessian.pk {invivoPKfit} | R Documentation |
Get Hessian matrixes
Description
Extract Hessian matrixes from a fitted 'pk' object
Usage
## S3 method for class 'pk'
get_hessian(obj, model = NULL, method = NULL, suppress.messages = TRUE, ...)
Arguments
obj |
A [pk] object |
model |
Optional: Specify one or more of the fitted models whose coefficients to return. If NULL (the default), coefficients will be returned for all of the models in 'obj$stat_model'. |
method |
Optional: Specify one or more of the [optimx::optimx()] methods whose coefficients to return. If NULL (the default), coefficients will be returned for all of the models in 'obj$settings_optimx$method'. |
suppress.messages |
Logical. 'TRUE' (the default) to suppress informative messages. 'FALSE' to see them. |
... |
Additional arguments. Not in use right now. |
Details
This function computes a numerical approximation to the model Hessian for each data group and each model in a fitted 'pk' object. The Hessian is the matrix of second derivatives of the model objective function with respect to each model parameter. 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.
Value
A dataframe with one row for each 'data_group', 'model' and 'method'. The remaining column is a 'list' column containing the Hessian for each row.
Author(s)
Caroline Ring and Gilberto Padilla Mercado
References
Gill J, King G. (2004) What to Do When Your Hessian is Not Invertible: Alternatives to Model Respecification in Nonlinear Estimation. Sociological Methods & Research 33(1):54-87. DOI: 10.1177/0049124103262681
See Also
Other methods for fitted pk objects:
AAFE.pk()
,
AFE.pk()
,
AIC.pk()
,
BIC.pk()
,
coef.pk()
,
coef_sd.pk()
,
eval_tkstats.pk()
,
get_fit.pk()
,
get_tkstats.pk()
,
logLik.pk()
,
predict.pk()
,
residuals.pk()
,
rmse.pk()
,
rsq.pk()