cv.glmnet计算模型的AIC与BIC

glmnet_cv_aicc <- function(fit, lambda = 'lambda.1se'){
  whlm <- which(fit$lambda == fit[[lambda]])
  with(fit$glmnet.fit,
       {
         # Null deviance (per observation). 
         # This is defined to be 2*(loglike_sat -loglike(Null)). 
         # The null model refers to the intercept model.
         tLL <- nulldev - nulldev * (1 - dev.ratio)[whlm] # nulldev - dev
         k <- df[whlm] # df means the number of nonzero coefficients
         n <- nobs # Number of observations.
         return(list('AIC' = - tLL + 2 * k + 2 * k * (k + 1) / (n - k - 1),
                     'BIC' = log(n) * k - tLL))
       })
}