BicZ {CorReg} | R Documentation |
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.
require(CorReg) 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)