Compute the BIC of a given structure
BicZ( X = X, Z = Z, Bic_null_vect = NULL, Bic_old = NULL, methode = 1, Zold = NULL, star = FALSE )
| X | the dataset |
|---|---|
| Z | binary adjacency matrix of the structure (size p) |
| Bic_null_vect | the BIC of the null hypothesis (used for independent variables) |
| Bic_old | BIC (vector) associated to Zold |
| methode | parameter for OLS (matrix inversion) methode_BIC parameter for OLS (matrix inversion) 1:householderQr, 2:colPivHouseholderQr |
| Zold | another structure with some common parts with Z (allows to compute only the differences, to be faster) |
| star | boolean defining wether classical BIC or BIC* (over-penalized by a hierarchical uniform assumption to avoid over-learning)is computed |
The vector of the BICs associated to each covariate (conditionnal distribution) according to the sub-regression structure.
data = mixture_generator(n = 15, p = 5, valid = 0) # dataset generation Z = data$Z # binary adjacency matrix that describes correlations within the dataset X = data$X_appr Bic_null_vect = density_estimation(X = X)$BIC_vect # Computes the BIC associated to each covariate (optional, BicZ can do it if not given as an input) # computes the BIC associated to the structure res = BicZ(X = X, Z = Z, Bic_null_vect = Bic_null_vect)