2014年7月20日 · k k = number of parameters in the model L L = likelihood Bayesian information criterion BIC is closely related with AIC.The AIC penalizes the number of parameters less strongly than does the BIC. I can see these two are used everywhere historically. But generalized cross validation (GCV) is new to me. How GCV can relate to BIC or AIC?

His treatment integrates GCV in general with applications in regression and logistic regression. If you look at the ESL book, p 244, you see basically the same symbology.

2021年3月9日 · And in this case, the GCV predictions should approximate the ones from REML with higher sample sizes? Finally, adding to on to the last point, I understand that in the case of mixed models, the different methods converge at high sample sizes.

2017年9月21日 · The GCV score, V V, therefore depends on the units of the response and/or on the specific data used in the model. As such you cannot compare, in a meaningful way, the GCV scores for the two models you showed in your example. The absolute value of the GCV score is interpretable as an estimation of the lack of fit of the model.

2022年8月7日 · I have a question about Generalized Additive Models. What is deviance explained, GCV score and scale est in GAM results? What do these indicators show?

2017年9月4日 · In mgcv there are various methods to finding the smoothing parameter, lambda, such as GCV and ML/REML. GCV works by minimizing predictive error, but is subject to under/over-smoothing. ML/REML are ...

These two packages are far more fully featured than lm.ridge() in the MASS package for such things. Anyway, λ = 0 λ = 0 implies zero penalty, hence the least squares estimates are optimal in the sense that they had the lowest GCV (generalised cross validation) score.

I am trying to figure out how to control the smoothing parameters in an mgcv::gam model. I have a binomial variable I am trying to model as primarily a function of x and y coordinates on a fixed gr...

2015年5月30日 · Following is the ridge regression example in MASS package: > head (longley) y GNP Unemployed Armed.Forces Population Year Employed 1947 83.0 234.289 235.6 159.0 107.608

2019年6月22日 · (Changing a comment to an answer.) Yes, you want the lambda that minimizes GCV. MASS's lm.ridge doesn't choose a default lambda sequence for you. Look at which talks about good default choices for lambda. Also, I'd suggest using cv.glmnet with alpha = 0 (meaning ridge penalty) from glmnet package which will do this cross validation with some good …