similar to: help needed with ridge regression and choice of lambda with lm.ridge!!!

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

Hi all, I have run a ridge regression on a data set 'final' 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

Unlike L1 (lasso) regression or elastic net (mixture of L1 and L2), L2 norm regression (ridge regression) does not select variables. Selection of variables would not work properly, and it's unclear why you would want to omit "apparently" weak variables anyway. Frank maths123 wrote > I have a .txt file containing a dataset with 500 samples. There are 10 > variables. > >

Hi all, I have run a ridge regression as follows: reg=lm.ridge(final$l~final$lag1+final$lag2+final$g+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

Dear all I'm familiarising myself with Ridge Regressions in R and the following is bugging me: How does one get p-values for the coefficients obtained from MASS::lm.ridge() output (for a given lambda)? Consider the example below (adapted from PRA [1]): > require(MASS) > data(longley) > gr <- lm.ridge(Employed ~ .,longley,lambda = seq(0,0.1,0.001)) > plot(gr) > select(gr)

Given the following data, I am plotting log.det ~ norm.beta, where the points depend on a parameter, lambda (but there is no functional form). I want to find the (x,y) positions along this curve corresponding to two special values of lambda lambda.HKB <- 0.004275357 lambda.LW <- 0.03229531 and draw reference lines at ~ -45 degrees (or normal to the curve) thru these points. How can I do

Hi There, Using t.test to test hypothesis about which one is greater, A or B? where A={10,20,30},B={25,30,15}. My question is which of the following conclusions is right? #################hypothesis testing 1 h0: A greater than or equal to B h1: A less than B below is splus code A=c(10,20,30) B=c(25,30,15) t.test(c(10,20,30),c(25,30,15),alternative="less") output: p-value=0.3359

Hi all I have read the lartc manual, I have read the some faqs but still don''t know how to achive the following: I have a 512kbps line which I share with 8 customers. Now what I have is everyone gets max 64kbps. That''s fine. DEV=ath0 DEV_MAX_RATE=6mbit DEV_RAT_LOW=64kbit DEV_RATE_HIGH=128kbit tc qdisc del dev $DEV root tc qdisc add dev $DEV root handle 1: cbq avpkt 1000

Hi guys, I've a problem by one of our replicated volumes. When i look on both bricks the size of them are euqal but if i take a look into the gluster quota informations 'gluster volume quota xxx list' i get a wrong usage size. I can't see any errors on my glusterfs or brick logs. Do anyone have a hint ? Cheers Heiko -- System Wizard anynines.com

Hello R-list, I am trying to calculate a ridge regression using first the *lm.ridge()* function from the MASS package and then applying the obtained Hoerl Kennard Baldwin (HKB) estimator as a penalty scalar to the *ols()* function provided by Frank Harrell in his Design package. It looks like this: > rrk1<-lm.ridge(lnbcpc ~ lntex + lnbeerp + lnwinep + lntemp + pop, subset(aa,

Dear all: Described below is a large data set problem (data size > 2G after unzipping, table delimited). I know R is not the appropriate tool for such task, anyway I did it on a server and get some straightforward problems. 1. The first is count.fields can count all the rows, however, when I tried to remove rows beyond 3/5 of the data,R says subscripts out of bounds, is there any option

Hi, When using the glmnet() function of the package glmnet, A series of coefficients is returned for a list of descending lambda values. I am unable to locate anything in the documentation that explains HOW this choice of lambda series is made. (There is documentation about how to choose my own, but I want to understand how the authors are doing it) Any ideas? -- Noah Silverman UCLA

does anyone know how to extract Shrinkage intensity lambda and lambda.var values after run cov.shrink(x)? thanks, KZ [[alternative HTML version deleted]]

Dear all, I want to get the likelihood (or AIC or BIC) of a ridge regression model using lm.ridge from the MASS library. Yet, I can't really find it. As lm.ridge does not return a standard fit object, it doesn't work with functions like e.g. BIC (nlme package). Is there a way around it? I would calculate it myself, but I'm not sure how to do that for a ridge regression. Thank you in

For an application of ridge regression, I need to get the covariance matrices of the estimated regression coefficients in addition to the coefficients for all values of the ridge contstant, lambda. I've studied the code in MASS:::lm.ridge, but don't see how to do this because the code is vectorized using one svd calculation. The relevant lines from lm.ridge, using X, Y are:

Dear All, I would like to ask a couple of questions on a LME model. I tested 4 selection lines at 4 food concentrations against a standard competitor stock. I had 3 replicate cages per selection line. In each cage I have 10 vials. I counted the number of wild type flies and competitor stock emerging in each vial. My main question is: is there any difference between selection lines? I did fit

Hi ZFS Team, I have a couple of questions... Assume that the maximum slab size that ZFS supports is x. (I am assuming there is a maximum.) An application does a (single) write(2) for 2x bytes. Does ZFS/COW guarantee that either all the 2x bytes are persistent or none at all? Consider a case where there is a panic after x bytes has gone to disk and the change propagated to the uber block. Do

Hi all, I am using glampath package for L1 regularized logistic regression. I have read the article " L1 regularization path algorithm for GLM" by park and Hastie (2006). One thing I can't understand that how to find best lambda for my prediction. I want to use that lambda for the prediction not the entire set. thanks. -- View this message in context:

Usually when using raw glmnet I let the implementation choose the lambdas. However when training via caret::train the lambda values are predetermined. Is there any way to have caret defer the lambda choices to caret::train and thus choose the optimal lambda dynamically? -- Yang Zhang http://yz.mit.edu/

Hello, using the lda-method from MASS-package I was wondering whether there is a built-in method for figuring out Wilks' Lambda? Searching the Web I found in the r-help archive a thread form june 2002, but it didn't help me. I understand I can use manova and its summary-method to get Wilks' Lambda on the screen, but I don't see the connection to lda from MASS. Or does