tuning_efron {pvEBayes} | R Documentation |
Select hyperparameter (p, c0) and obtain the optimal efron model based on AIC and BIC
Description
Select hyperparameter (p, c0) and obtain the optimal efron model based on AIC and BIC
Usage
tuning_efron(
contin_table,
p_vec = NULL,
c0_vec = NULL,
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). |
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. |
Value
a list of fitted models with hyperparameter alpha selected by AIC or BIC.
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.