pvEBayes_tune {pvEBayes} | R Documentation |
Select hyperparameter and obtain the optimal general-gamma or efron model based on AIC and BIC
Description
This function performs hyperparameter tuning for the general-gamma or Efron model using AIC or BIC. For a given AE-drug contingency table, the function fits a series of models across a grid of candidate hyperparameter values and computes their AIC and BIC. The models with the lowest AIC or BIC values are selected as the optimal fits.
Usage
pvEBayes_tune(
contin_table,
model = "general-gamma",
alpha_vec = NULL,
p_vec = NULL,
c0_vec = NULL,
use_AIC = TRUE,
n_posterior_draws = 1000,
return_all_fit = FALSE,
return_all_AIC = TRUE,
return_all_BIC = TRUE
)
Arguments
contin_table |
an IxJ contingency table showing pairwise counts of adverse events for I AEs (along the rows) and J drugs (along the columns). |
model |
the model to be tuned. Available models are "general-gamma" and "efron". Default to "general-gamma". |
alpha_vec |
vector of hyperparameter alpha values to be selected. Alpha is a hyperparameter in general-gamma model which is numeric and between 0 and 1. If is NULL, a default set of alpha values (0, 0.1, 0.3, 0.5, 0.7, 0.9) will be used. |
p_vec |
vector of hyperparameter p values to be selected. p is a hyperparameter in "efron" model which should be a positive integer. If is NULL, a default set of p values (80, 100, 120, 150, 200) will be used. |
c0_vec |
vector of hyperparameter c0 values to be selected. c0 is a hyperparameter in "efron" model which should be a positive number. If is NULL, a default set of c0 values (0.001, 0.01, 0.1, 0.2, 0.5) will be used. |
use_AIC |
logical, indicating whether AIC or BIC is used. Default to be TRUE. |
n_posterior_draws |
number of posterior draws for each AE-drug combination. |
return_all_fit |
logical, indicating whether to return all the fitted model under the selection. Default to be FALSE. |
return_all_AIC |
logical, indicating whether to return AIC values for each fitted model under the selection. Default to be TRUE. |
return_all_BIC |
logical, indicating whether to return BIC values for each fitted model under the selection. Default to be TRUE. |
Value
The function returns an S3 object of class pvEBayes
containing the selected
estimated model parameters as well as posterior draws for each AE-drug
combination if the number of posterior draws is specified.
References
Akaike H. A new look at the statistical model identification. IEEE Transactions on Automatic Control.
2003; 19(6):716-23.
Schwarz G. Estimating the dimension of a model. The Annals of Statistics. 1978; 1:461-4.
Tan Y, Markatou M and Chakraborty S. Flexible Empirical Bayesian Approaches to Pharmacovigilance for Simultaneous Signal Detection and Signal Strength Estimation in Spontaneous Reporting Systems Data. arXiv preprint. 2025; arXiv:2502.09816.
Examples
fit <- pvEBayes_tune(statin2025_44, model = "general-gamma")