similar to: Off topic -- large data sets. Was RE: 64 Bit R Background Question

In message <200502231143.j1NBTtYT030301 at hypatia.math.ethz.ch>, r-help- request at stat.math.ethz.ch writes >The R platform that I installed on my Windows XP crashes everytime that >I try to run some sophisticated graphics (e.g. Demo Graphics). Is that >to do with the configuration? Shall I reinstall it? You may have a buggy video driver. If you go to Control Panel, Display,

I am fairly new to R. I find it surprising that f <- function(x,a) {x-a} uniroot(f, c(0,1), a=.5) works, but integrate(f, 0, 1, a=.5) gives an error: Error in integrate(f, 0, 1, a = 0.5) : argument 4 matches multiple formal arguments What is the best way of avoiding such surprises? Is there a way of telling integrate() that the 'a' argument is for f()? If I wrote my own function

Dear R Users, I'm very very interested in learning how to use R to carry out a classification of data using discriminant function analysis. I've found the MASS package and the lda function, but the examples in the help system are a bit over my head. I'm not exactly sure how to interpret the output, for example, of if the inputs I've chosen are best suited to my needs. I

I have been running SharpEye under Wine for a few weeks now. There are a couple of problems 1) The fonts don't always appear correct. Sometimes they correctly show as Arial, sometimes as SmallCaps 2) Can't get TWAIN to work - see earlier message 3) The pull down menus display as XMFile etc. instead of File etc. The author of SharpEye says this is a bug in Wine. -Nigel -- Nigel Horne.

Hi, I'm trying to use SharpEye2 under Wine: that is a Musical Optical Recognition (musical OCR) program, getting a scanned image and outputting a digital score - look at http://www.visiv.co.uk. It is probably the best such program among the few existing ones and the only thing I miss from Windows. Anyway, the installation goes perfectly, but when I try to run it, a window appears saying

Hello! I'm interested in getting a Chinese optical character recognition program to run under wine. http://www.twinbridge.com/Products/SharpEye/ocr.html According to the web site, it may run on Windows 95 as well as the old Windows 3. Is it therefore safe to assume that it will run under wine? Surely it should be able to handle anything that can run on Windows 3 right? Any advice will

Hi, Let's see if any of the questions gets answered or at least I get ponte dto something that can help me. I followed this wiki: http://wiki.samba.org/index.php/Samba4/HOWTO/Join_a_domain_as_a_DC#Getting_ready_for_joining_Samba_as_a_DC_to_an_existing_domain I have my S4 domain running, I compiled and installed another S4 to replicate the first server and joined successfully to the

Martin Otten did most of the work to get Speex up and running for our games about a month ago, and we just recently released it to the Steam beta. So far it appears to be working pretty well, and we're interested in doing more work using Speex going forward. I'd love to hear any feedback that people have using it, especially at the different quality settings. The information on how it

The Wine development release 1.5.7 is now available. What's new in this release (see below for details): - New version of the Gecko engine based on Firefox 13. - Dynamic device support with UDisks2. - More stream classes implemented in the C++ runtime. - Support for metadata in TIFF files. - Fleshed out WBEM implementation. - Improved support for printer paper sizes. - Various

Dear Robert, The differences have to do with diffent scaling defaults. lrm by default standardizes the covariates to unit sd before applying penalization. penalized by default does not do any standardization, but if asked standardizes on unit second central moment. In your example: x = c(-2, -2, -2, -2, -1, -1, -1, 2, 2, 2, 3, 3, 3, 3) z = c(0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1) You

Hello, Thank you for probably not so new question, but i am new to R. Does any of packages have something like glm+regularization? So far i see probably something close to that as a ridge regression in MASS but I think i need something like GLM, in particular binomial regularized versions of polynomial regression. Also I am not sure how some of the K-fold crossvalidation helpers out there

The following R code using different packages gives the same results for a simple logistic regression without regularization, but different results with regularization. This may just be a matter of different scaling of the regularization parameters, but if anyone familiar with these packages has insight into why the results differ, I'd appreciate hearing about it. I'm new to

We have uploaded to CRAN the first version of glmpath, which fits the L1 regularization path for generalized linear models. The lars package fits the entire piecewise-linear L1 regularization path for the lasso. The coefficient paths for L1 regularized glms, however, are not piecewise linear. glmpath uses convex optimization - in particular predictor-corrector methods- to fit the

We have uploaded to CRAN the first version of glmpath, which fits the L1 regularization path for generalized linear models. The lars package fits the entire piecewise-linear L1 regularization path for the lasso. The coefficient paths for L1 regularized glms, however, are not piecewise linear. glmpath uses convex optimization - in particular predictor-corrector methods- to fit the

Dear all: In what I am doing I sometimes get a (Hessian) matrix that has a couple of tiny negative eigenvalues (e.g. -6 * 10^-17). So, I can't run a Cholesky decomp on it - but I need to. Is there an established way to regularize my (Hessian) matrix (e.g., via some transformation) that would allow me to get a semi-positive definite matrix to be used in Cholesky decomp? Or should I try some

We have uploaded to CRAN a new version of glmpath, a package which fits the L1 regularization path for generalized linear models. The revision includes: - coxpath, a function for fitting the L1-regularization path for the Cox ph model; - bootstrap functions for analyzing sparse solutions; - the ability to mix in L2 regularization along with L1 (elasticnet). We have also completed a report that

We have uploaded to CRAN a new version of glmpath, a package which fits the L1 regularization path for generalized linear models. The revision includes: - coxpath, a function for fitting the L1-regularization path for the Cox ph model; - bootstrap functions for analyzing sparse solutions; - the ability to mix in L2 regularization along with L1 (elasticnet). We have also completed a report that

This update adds an intercept option (by popular request) - now one can fit a model without an intercept Glmnet is a package that fits the regularization path for a number of generalized linear models, with with "elastic net" regularization (tunable mixture of L1 and L2 penalties). Glmnet uses pathwise coordinate descent, and is very fast. The current list of models covered are:

This update adds an intercept option (by popular request) - now one can fit a model without an intercept Glmnet is a package that fits the regularization path for a number of generalized linear models, with with "elastic net" regularization (tunable mixture of L1 and L2 penalties). Glmnet uses pathwise coordinate descent, and is very fast. The current list of models covered are:

We have put a new package sparsenet on CRAN. Sparsenet fits regularization paths for sparse model selection via coordinate descent, using a penalized least-squares framework and a non-convex penalty. The package is based on our JASA paper Rahul Mazumder, Jerome Friedman and Trevor Hastie: SparseNet : Coordinate Descent with Non-Convex Penalties. (JASA 2011)