similar to: issue with model.frame()

>>>>> Berry, Charles <ccberry at ucsd.edu> >>>>> on Tue, 1 May 2018 16:43:18 +0000 writes: >> On May 1, 2018, at 6:11 AM, Therneau, Terry M., Ph.D. via R-devel <r-devel at r-project.org> wrote: >> >> A user sent me an example where coxph fails, and the root of the failure is a case where names(mf) is not equal to the

> On May 1, 2018, at 6:11 AM, Therneau, Terry M., Ph.D. via R-devel <r-devel at r-project.org> wrote: > > A user sent me an example where coxph fails, and the root of the failure is a case where names(mf) is not equal to the term.labels attribute of the formula -- the latter has an extraneous newline. Here is an example that does not use the survival library. > > # first

Great catch. I'm very reluctant to use my own model.frame, since that locks me into tracking all the base R changes, potentially breaking survival in a bad way if I miss one. But, this shows me clearly what the issue is and will allow me to think about it. Another solution for the user is to use multiple ridge() calls to break it up; since he/she was using a fixed tuning parameter the

[Automatic forwarding from R-bugs failed. This message has been manually forwarded.] Hi all! I'm trying to add Thomas Lumley's defmacro() function Lumley T. "Programmer's Niche: Macros in {R}", R News, 2001, Vol 1, No. 3, pp 11--13, \url{http://CRAN.R-project.org/doc/Rnews/} to the gtools package (provided that Thomas gives his OK). And I've encountered an error in

Hi, I am trying to do multinomial regression using the glmnet package, but the following gives me an error (for no reason apparent to me): library(glmnet) cv.glmnet(x=matrix(c(1,2,3,4,5,6,1,2,3,4,5,6), nrow=6),y=as.factor(c(1,2,1,2,3,3)),family='multinomial',alpha=0.5, nfolds=2) The error i get is: Error in if (outlist$msg != "Unknown error") return(outlist) : argument is of

Hi Everybody! I must start with a declaration that I am a sparse user of R. I am creating a credit scorecard using a dataset which has a variable depicting actual credit history (good/bad) and 41 other variables of yes/no type. The procedure I am asked to follow is to use a penalized logistic procedure for variable selection. I have located the package "glmnet" which gives the complete

Dear everyone I'm dealing with a problem related to Poisson Ridge Regression. If anyone can help me in this regard by telling if any changes in the source code of "glm.fit" may help -- Regards Umesh Khatri

Hi, I am trying to do logistic regression for data of 104 patients, which have one outcome (yes or no) and 15 variables (9 categorical factors [yes or no] and 6 continuous variables). Number of yes outcome is 25. Twenty-five events and 15 variables mean events per variable is much less than 10. Therefore, I tried to analyze the data with penalized regression method. I would like please some of the

Hi, I would like to write a function which has the following syntax: myfn <- function(formula, ftime, fstatus, data) { # step 1: obtain terms in `formula' from dataframe `data' # step 2: obtain ftime from `data' # step 3: obtain fstatus from `data' # step 4: do model estimation # step 5: return results } The user would call this function as: myfn(formula=myform,

Hello, Dovecot keeps crashing at login time. Things were fine on 2.2.2 Fatal: master: service(imap): child 5014 killed with signal 11 Here is the trace: Core was generated by `imap'. Program terminated with signal 11, Segmentation fault. #0 0x00000000105cdfdf in mailbox_list_get_storage (list=0x7fffffffe310, vname=0x10a04ab0 "INBOX/spam", storage_r=0x7fffffffe308) at

We're currently running 2.1.16. I've been wanting to update to a 2.2.X version, but have been running into problems, even with the latest (2.2.12). I'm not sure at what version this issue appeared, but I believe I've tried 2.2.10, 2.2.11, and now 2.2.12. We have a shared maildir setup. With that configuration in place and with a user in the dovecot-acl file, I can do the

Hello, I'm trying to select features of cetain numbers(like 100 out of 1000) via LASSO, based on multinomial model, however, it seems the glmnet package provides a very sparse estimation of coefficients(most of coefficients are 0), which selects very few number of variables, like only 10, based on my easy dataset. I try to connect the choice of lambda to the selecting

On Wed, Apr 3, 2019 at 9:37 PM Mark Moseley <moseleymark at gmail.com> wrote: > > On Wed, Mar 20, 2019 at 2:13 PM Mark Moseley <moseleymark at gmail.com> > wrote: > >> Just hoping to get some dev eyes on this. I'm incredibly reluctant to >> throw the word 'bug' around >> (since 99 times out of 100, it's not -- it's almost always the

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 all. I'm looking for robust ways of building lagged variables in a dataset with multiple individuals. Consider a dataset with variables like the following: ## set.seed(123) d <- data.frame(id = rep(1:2, each=3), time=rep(1:3, 2), value=rnorm(6)) ## >d id time value 1 1 1 -0.56047565 2 1 2 -0.23017749 3 1 3 1.55870831 4 2 1 0.07050839 5 2 2 0.12928774 6

We are workin on building a logistic regression using 1. We are doing a logistic regression with binary outcome variable using a set of predictors that include 8 continuous and 8 category predictors 2. We are trying to implement interaction between two variables (continuous and category or just continuous) The dataset is 200,000 rows and we are using glmnet, how can we model those two points ?

Heya, We are expiriencing issues with dovecot 2.1.1 on Linux with weird filenames in home directory of username. We are using mbox IMAP folders, with no special changes (mail_location = mbox:~/:INBOX=%h/.mailbox). Mar 6 13:37:17 machine dovecot: imap(username): Panic: file mail-storage.c: line 628 (mailbox_alloc): assertion failed: (uni_utf8_str_is_valid(vname)) Mar 6 13:37:17 machine dovecot:

The model.frame function has trouble with a certain type of really long formula. Here is a test: tname <- paste('var', 1:50, sep='') tmat <- matrix(rnorm(500), ncol=50, dimnames=list(NULL, tname)) tdata <- data.frame(tmat) temp1 <- paste( paste(tname, tname, sep='='), collapse=', ') temp2 <- paste("~1 + cbind(", temp1, ")")

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, 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]]