similar to: Rock Ridge for core/fs/iso9660

On 04/25/2013 07:03 AM, syslinux-bot for Thomas Schmitt wrote: > Commit-ID: 5de463f724da515fd6c5ea49ded6dde178362181 > Gitweb: http://www.syslinux.org/commit/5de463f724da515fd6c5ea49ded6dde178362181 > Author: Thomas Schmitt <scdbackup at gmx.net> > AuthorDate: Thu, 4 Apr 2013 20:02:37 +0200 > Committer: Matt Fleming <matt.fleming at intel.com> > CommitDate:

Hi, i began to implement a common lookup function for SUSP and Rock Ridge entries: /* Obtain the payload bytes of all SUSP entries with a given signature. @param fs The data source from which to read CE blocks. @param dir_rec Memory containing the whole ISO 9660 directory record. @param sig Two characters of SUSP signature. E.g. "NM", "ER", ...

Hi, i cannot yet see my most recent bug fix patch applied to http://git.kernel.org/cgit/boot/syslinux/syslinux.git/log/?h=rockridge The fixed problem could lead to memory faults. http://www.syslinux.org/archives/2013-April/019790.html Have a nice day :) Thomas

Compile issue with tools/libfsimage/iso9660 char vs unsigned char signedness causes a warning when compiling iso9660 (xen-unstable). This patch changes the unsigned char * for char *. Signed-off-by: Mathieu Desnoyers <mathieu.desnoyers@polymtl.ca> diff -r 8eff89a69521 tools/libfsimage/iso9660/fsys_iso9660.c --- a/tools/libfsimage/iso9660/fsys_iso9660.c Fri Mar 02 18:42:00 2007 -0500 +++

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:

The attached patch adds iso9660 detection support to fstype. Signed-off-by: David H?rdeman <david at hardeman.nu> -- fstype.c | 17 +++++++++++++++++ iso9660_sb.h | 24 ++++++++++++++++++++++++ 2 files changed, 41 insertions(+) -------------- next part -------------- diff -Nru klibc/usr/kinit/fstype/fstype.c klibc-hack/usr/kinit/fstype/fstype.c ---

Hi, I'm a beginner in the field, I have to perform the ridge regression with lm.ridge for many datasets, and I wanted to do it in an automatic way. In which way I can automatically choose lambda ? As said, right now I'm using lm.ridge MASS function, which I found quite simple and fast, and I've seen that among the returned values there are HKB estimate of the ridge constant and L-W

It seems to me that summary for ridge coxph() prints summary but returns NULL. It is not a big issue because one can calculate statistics directly from a coxph.object. However, for some reason the score test is not calculated for ridge coxph(), i.e score nor rscore components are not included in the coxph object when ridge is specified. Please find the code below. I use 2.9.2 R with 2.35-4 version

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

Hello, I have questions regarding penalized Cox regression using survival package (functions coxph() and ridge()). I am using R 2.8.0 on Ubuntu Linux and survival package version 2.35-4. Question 1. Consider the following example from help(ridge): > fit1 <- coxph(Surv(futime, fustat) ~ rx + ridge(age, ecog.ps, theta=1), ovarian) As I understand, this builds a model in which `rx' is

Dear all, I need to calculate likelihood ratio test for ridge regression. In February I have reported a bug where coxph returns unpenalized log-likelihood for final beta estimates for ridge coxph regression. In high-dimensional settings ridge regression models usually fail for lower values of lambda. As the result of it, in such settings the ridge regressions have higher values of lambda (e.g.

Hi Nick, I think that the problem here is your use of $coef to extract the coefficients of the ridge regression. The help for lm.ridge states that coef is a "matrix of coefficients, one row for each value of lambda. Note that these are not on the original scale and are for use by the coef method." I ran a small test with simulated data, code is copied below, and indeed the output from

I asked you before, but in case you missed it: Are you looking at the right place in SPSS output? The UNstandardized coefficients should be comparable to R, i.e. the "B" column, not "Beta". -pd > On 5 May 2017, at 01:58 , Nick Brown <nick.brown at free.fr> wrote: > > Hi Simon, > > Yes, if I uses coefficients() I get the same results for lm() and

Hello, I have posted this mail a few days ago but I did it wrong, I hope is right now: I have the following doubts related with lm.ridge, from MASS package. To show the problem using the Longley example, I have the following doubts: First: I think coefficients from lm(Employed~.,data=longley) should be equal coefficients from lm.ridge(Employed~.,data=longley, lambda=0) why it does not happen?

It seems to me that R returns the unpenalized log-likelihood for the ratio likelihood test when ridge regression Cox proportional model is implemented. Is this as expected? In the example below, if I am not mistaken, fit$loglik[2] is unpenalized log-likelihood for the final estimates of coefficients. I would expect to get the penalized log-likelihood. I would like to check if this is as expected.

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)

Dear all, I considered an ordinary ridge regression problem. I followed three different ways: 1. estimate beta without any standardization 2. estimate standardized beta (standardizing X and y) and then again convert back 3. estimate beta using lm.ridge() function X<-matrix(c(1,2,9,3,2,4,7,2,3,5,9,1),4,3) y<-t(as.matrix(cbind(2,3,4,5))) n<-nrow(X) p<-ncol(X) #Without

I had no problems running regression models in SPSS and R that yielded the same results for these data. The difference you are observing is from fitting different models. In R, you fitted: res <- lm(DEPRESSION ~ ZMEAN_PA * ZDIVERSITY_PA, data=dat) summary(res) The interaction term is the product of ZMEAN_PA and ZDIVERSITY_PA. This is not a standardized variable itself and not the same as

I am trying to understand the code for lm.ridge from the MASS package. Here is the part I am having trouble understanding: if(Inter <- attr(Terms, "intercept")) { Xm <- colMeans(X[, -Inter]) Ym <- mean(Y) p <- p - 1 X <- X[, -Inter] - rep(Xm, rep(n, p)) Y <- Y - Ym } else Ym <- Xm <- NA Xscale <- drop(rep(1/n, n) %*% X^2)^0.5 X <- X/rep(Xscale, rep.int(n,

On Wed, 2007-02-21 at 14:46 +0000, Xen staging patchbot-unstable wrote: > # HG changeset patch > # User john.levon@xxx > # Date 1172012044 28800 > # Node ID fe3e024e38f8323c311fbd61710eff3c4b92f514 > # Parent bca284f67702cf46502809f29eb634e2ab6d294f > Add iso9660 support to libfsimage. There seems to be some sign-age problems introduced here. I''m surprised the