similar to: CV and GCV for finding smoothness parameter

Full_Name: Andrew Robinson Version: 2.6.2 Patched (2008-02-12 r44439) OS: FreeBSD 6.3-RC1 Submission from: (NULL) (211.28.206.186) I believe that the computation for GCV is incorrect in the lm.ridge function in MASS. >From lm.ridge: GCV <- colSums((Y - X %*% coef)^2)/ (n - colSums(matrix(d^2/div, dx)))^2 The denominator does not tally with the formula on p. 141 of Ripley's

hello to everybody: I would to know with ranges of GCV or UBRE values can be considered as adequate to consider a GAM as correct Thanks in advance -- David Nogu?s Bravo Functional Ecology and Biodiversity Department Pyrenean Institute of Ecology Spanish Research Council Av. Monta?ana 1005 Zaragoza - CP 50059 976716030 - 976716019 (fax)

Hi, I've been using gamm4 to build GAMMs for exploring environmental influences on genetic ancestry. Things have gone well and I have 2 very straightforward questions: 1. I've used method=REML. Am I correct that this is an alternative method for estimating the smooth functions in GAMMs rather than GCV that is often used for GAMs? I've read up on REML and it makes sense, but I'm

Hi all, I have run a ridge regression as follows: reg=lm.ridge(final$l~final$lag1+final$lag2+final$g+final$u, lambda=seq(0,10,0.01)) Then I enter : select(reg) and it returns: modified HKB estimator is 19.3409 modified L-W estimator is 36.18617 smallest value of GCV at 10 I think it means that it is advisable to

Hello, I have a question regarding model selection and dropping of terms for GAMs fitted with package mgcv. I am following the approach suggested in Wood (2001), Wood and Augustin (2002). I fitted a saturated model, and I find from the plots that for two of the covariates, 1. The confidence interval includes 0 almost everywhere 2. The degrees of freedom are NOT close to 1 3. The partial

I am using gam in mgcv (1.3-22) and trying to use gcv to help with model selection. However, I'm a little confused by the process of assessing GCV scores based on their magnitude (or on relative changes in magnitude). Differences in GCV scores often seem "obvious" with my poisson gams but with negative binomial, the decision seems less clear. My data represent a similar pattern as

Dear R-listers, I am now using principal curves for data analysis. The definition of it required the accurate concept of curve continuity (C0, C1, ..., CK) and curve smoothness. So if anyone can introduce the exact definition of continuous for curves and what is the most popular textbooks for me? Thanks for ur time and kind attention. Fred [[alternative HTML version deleted]]

I have 3 sets of Cartesian data, one is 'original' data and the other 2 are "smoothed"data. The smoothed data is the result of applying a smoothing algorithm to the original.One set of smoothed data is the 'old' algorithm and the other set is the 'new' algorithm. Does R have the capability of telling me which data is "smoother"? Example data (subsets