similar to: Penalized Logistic Regression - Query

Hi, I couldn't find a package to fit a penalized (lasso/ridge) Gamma regression model. Does anybody know any? Thanks in advance, Lars. [[alternative HTML version deleted]]

Dear R users: Recently, I learn to use penalized logistic regression. Two packages (penalized and glmnet) have the function of lasso. So I write these code. However, I got different results of coef. Can someone kindly explain. # lasso using penalized library(penalized) pena.fit2<-penalized(HRLNM,penalized=~CN+NoSus,lambda1=1,model="logistic",standardize=TRUE) pena.fit2

Dear R experts, The lrm function in the Design package can perform penalized (Ridge) logistic regression. It is my understanding that the ridge solutions are not equivalent under scaling of the inputs, so one normally standardizes the inputs. Do you know if input standardization is done internally in lrm or I would have to do it prior to applying this function. Also, as I'm new in R (coming

Dear List, I'm running simulations using the glmnet package. I need to use an 'automated' method for model selection at each iteration of the simulation. The cv.glmnet function in the same package is handy for that purpose. However, in my simulation I have p >> N, and in some cases the selected model from cv.glmet is essentially shrinking all coefficients to zero. In this case,

Hi, Few days ago I posted a question to r-sig-finance, which I thought would be an easy one. To my surprise I have received no replies, which makes me think that it is either harder than I thought, or that it makes no sense. I am reposting the message (with some modifications) on the R-help in a hope to get some leads, suggestions for alternatives, etc. My apologies to those who had seen this on

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

Hi All, I'm planning to submit my first package to R, and although I read all the documentation, I'm not very clear on the following 2 items, from which I'd appreciate your guidance: 1)I understand it is suggested to use the R dev version to build the package. Which one specifically should I use to build a package on a Mac OS? How about package dependencies, which version should I

Christoph Lehman had problems with seperated data in two-class logistic regression. One useful little trick is to penalize the logistic regression using a quadratic penalty on the coefficients. I am sure there are functions in the R contributed libraries to do this; otherwise it is easy to achieve via IRLS using ridge regressions. Then even though the data are separated, the penalized

Hi All, I didn't have much success through my Google search in finding any active R-related projects to create a wrapper around TensorFlow in R. Anyone know if this is on the go? Thanks, Axel. [[alternative HTML version deleted]]

Hi All, I didn't have much success through my Google search in finding any active R-related projects to create a wrapper around TensorFlow in R. Anyone know if this is on the go? Thanks, Axel. [[alternative HTML version deleted]]

Dear List, The gbm package on Win 7 produces different results for the relative importance of input variables in R 32-bit relative to R 64-bit. Any idea why? Any idea which one is correct? Based on this example, it looks like the relative importance of 2 perfectly correlated predictors is "diluted" by half in 32-bit, whereas in 64-bit, one of these predictors gets all the importance

Dear List, I'm trying to solve this simple optimization problem in R. The parameters are the exponents to the matrix mm. The constraints specify that each row of the parameter matrix should sum to 1 and their product to 0. I don't understand why the constraints are not satisfied at the solution. I must be misinterpreting how to specify the constrains somehow. library(alabama) ff <-

Dear R users, I know this issue came up in the list several times. I’m currently running R on 32-bit on Windows and due to memory limitation problems would like to move to a 64-bit environment. I’m exploring my options and would appreciate your expertise: 1) Windows 64-bit: Prof. Brian Ripley recently posted the experimental built of R for win 64-bit. I’ll appreciate any feedback on

Dear List, I'm trying to add a function that calls a Shiny App in my R package. The issue is that within my function, I'm creating objects that I'd like to pass to the app. For instance, from the example below, I'm getting "Error: object 'xs' not found". How can I pass "xs" explicitly to shinyApp()? *Under R directory:* myApp <- function(x, ...) {

Hi, I was recently plowing through the docs of the quantreg package by Roger Koenker and came across the total variation penalty approach to 1-dimensional spline fitting. I googled around a bit and have found some papers originated in the image processing community, but (apart from Roger's papers) no paper that would discuss its statistical aspects. I have a couple of questions in this

Hi, I'm looking to move from Windows into a 64-bit Linux environment. Which is the best Linux Flavor to use within R? To install R on this environment, do I need to do any compiling? Thanks all! Axel. [[alternative HTML version deleted]]

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

Dear R list, From the lrm() binary logistic model we derived the G2 value or the likelihood-ratio chi-squared statistic given as L.R. model, in the output of the lrm(). How can this value be penalized for non-linearity (we used splines in the lrm function)? lrm.iRVI <- lrm(arson ~ rcs(iRVI,5), penalty=list(simple=10,nonlinear=100,nonlinear.interaction=4)) This didn’t work

Dear Axel, I've used environment for such problems. assign("xs", xs, envir = my.env) in the myApp function get("xs", envir = my.env) in the server function Best regards, ir. Thierry Onkelinx Statisticus/ Statiscian Vlaamse Overheid / Government of Flanders INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND FOREST Team Biometrie &

Dear all, I am using "penalized" package for "Ridge" regression. I do not know how can I get regression coefficients using that package . Please help me. Thanks -- Linda Garcia [[alternative HTML version deleted]]