similar to: Re: Logistic Regression

Hello, in the paper "Avoiding the effects of concurvity in GAM's .." of Figueiras et al. (2003) it is mentioned that in GLM collinearity is taken into account in the calc of se but not in GAM (-> results in confidence interval too narrow, p-value understated, GAM S-Plus version). I haven't found any references to GAM and concurvity or collinearity on the R page. And I

Hi, Is it possible to compare two GAM objects created with the gam() function from the mgcv package. I use a slightly modified version of anova.glm() named anova.gam(), modified from John Fox (2002). It often gives me some aberant responses, especially with "F" test. I use a quasibinomial model and scale (dispersion) is calculated and used in the calculation of the F value. Does someone

Hi, In the help files in the ?mgcv package for the gam.control() function, there is an option irls.reg. The help files describe this option as: For most models this should be 0. The iteratively re-weighted least squares method by which GAMs are fitted can fail to converge in some circumstances. For example, data with many zeroes can cause problems in a model with a log link, because a mean of

I have contributed a "gam" library to CRAN, which implements "Generalized Additive Models". This implementation follows closely the description in the GAM chapter 7 of the "white" book "Statistical Models in S" (Chambers & Hastie (eds), 1992, Wadsworth), as well as the philosophy in "Generalized Additive Models" (Hastie & Tibshirani 1990,

I have contributed a "gam" library to CRAN, which implements "Generalized Additive Models". This implementation follows closely the description in the GAM chapter 7 of the "white" book "Statistical Models in S" (Chambers & Hastie (eds), 1992, Wadsworth), as well as the philosophy in "Generalized Additive Models" (Hastie & Tibshirani 1990,

Hola a todos, Gracias por avisar Carlos. Intentaré formar un grupo en Logroño. Belén Cillero Jiménez Técnico de Estadística Instituto de Estadística de La Rioja bcillero en larioja.org o?s?? ol ??d???s s???? ou ,so?u??s?p sop??lns?? s??snq ?S ________________________________________ De: r-help-es-bounces en r-project.org [r-help-es-bounces en r-project.org] en nombre de r-help-es-request

This works: > model.matrix(~I(pos>3),data=data.frame(pos=c(1:5))) (Intercept) I(pos > 3)TRUE 1 1 0 2 1 0 3 1 0 4 1 1 5 1 1 attr(,"assign") [1] 0 1 attr(,"contrasts") attr(,"contrasts")$"I(pos > 3)" [1] "contr.treatment"

It would be nice to have a date stamp on an object. In S/Splus this was always available, because objects were files. I have looked around, but I presume this information is not available. -------------------------------------------------------------------- Trevor Hastie hastie at stanford.edu Professor, Department of Statistics, Stanford University Phone:

Hello. Douglas Bates has explained in a previous posting to R why he does not output residual degrees of freedom, F values and probabilities in the mixed model (lmer) function: because the usual degrees of freedom (obs - fixed df -1) are not exact and are really only upper bounds. I am interpreting what he said but I am not a professional statistician, so I might be getting this wrong... Does

Dear R users, I am using "enet" package in R for applying "elastic net" method. In elastic net, two penalities are applied one is lambda1 for LASSO and lambda2 for ridge ( zou, 2005) penalty. But while running the analysis, I realised tht, I optimised only one lambda. ( even when I looked at the example in R, they used only one penality) So, I am

Like many, I am sure, I get R-Help in digest form. Its easy enough to browse the subject lines, but then if an entry interests you, you have to embark on this tedious search or scroll to find it. It would be great to have a "clickable" digest, where the topics list is a set of pointers, and clicking on a topic takes you to that entry. I can think of at least one way to do this via

I installed R version 2.11.0 (2010-04-22) on may macbook (snow leopard) and run R from within emacs Now when I try to get help, I get > ?lm (in the new "help" window) Error in help("lm", htmlhelp = FALSE) : unused argument(s) (htmlhelp = FALSE) Help! p.s. I am running: This is GNU Emacs 22.2.50.1 (i386-apple-darwin9.4.0, Carbon Version 1.6.0) of 2008-07-17 on

Hi all, Could anybody please help me understand AIC and BIC and especially why do they make sense? Furthermore, I am trying to devise a new metric related to the model selection in the financial asset management industry. As you know the industry uses Sharpe Ratio as the main performance benchmark, which is the annualized mean of returns divided by the annualized standard deviation of returns.

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:

Does anyone know whether there is an R version of the S-Plus software that can be downloaded from the website of the book Elements of Statistical Learning by Hastie, Tibshirani and Friedman? Rob Potharst -- ********************************************************** Dr. Rob Potharst Lecturer in Computer Science Erasmus University email: potharst at few.eur.nl P.O. Box 1738

This is a new version of glmnet, that incorporates some bug fixes and speedups. * a new convergence criterion which which offers 10x or more speedups for saturated fits (mainly effects logistic, Poisson and Cox) * one can now predict directly from a cv.object - see the help files for cv.glmnet and predict.cv.glmnet * other new methods are deviance() for "glmnet" and coef() for

glmnet is a package that fits the regularization path for linear, two- and multi-class logistic regression models with "elastic net" regularization (tunable mixture of L1 and L2 penalties). glmnet uses pathwise coordinate descent, and is very fast. Some of the features of glmnet: * by default it computes the path at 100 uniformly spaced (on the log scale) values of the

glmnet is a package that fits the regularization path for linear, two- and multi-class logistic regression models with "elastic net" regularization (tunable mixture of L1 and L2 penalties). glmnet uses pathwise coordinate descent, and is very fast. Some of the features of glmnet: * by default it computes the path at 100 uniformly spaced (on the log scale) values of the

glmnet_1.2 has been uploaded to CRAN. This is a major upgrade, with the following additional features: * poisson family, with dense or sparse x * Cox proportional hazards family, for dense x * wide range of cross-validation features. All models have several criteria for cross-validation. These include deviance, mean absolute error, misclassification error and "auc" for logistic or