pvEBayes (version 0.2.1)

tuning_efron: 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,
  rtol_efron = 1e-10
)

Value

a list of fitted models with hyperparameter alpha selected by AIC or BIC.

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.

rtol_efron

a tolerance parameter used when 'efron' model is fitted. Default to 1e-10. See 'stats::nlminb' for detail.

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.