similar to: Issue with predict() for glm models

Could you just use lines(newX, myPred, col=2) -----Original Message----- From: r-help-bounces at stat.math.ethz.ch [mailto:r-help-bounces at stat.math.ethz.ch]On Behalf Of Paul Johnson Sent: Thursday, September 23, 2004 10:3 AM To: r help Subject: followup: Re: [R] Issue with predict() for glm models I have a follow up question that fits with this thread. Can you force an overlaid plot

Hi all, Just migrated users to a samba 4 server on built on Solaris 11 samba pkg. Some users are mapping all required drives (G:, H:, and I:) but some are not able to map them. The affected users user log files indicate that the %g variable is being translated to '-1' instead of the users Unix group. I would welcome any feedback or suggestions on how to resolve this issue. >From my

Hi all, Samba 2.2.8a is currently running on Solaris 8 as a member of a domain. For testing purposes I have installed Samba 3.0.10 on a test server (Solaris 9) a a member of a Workgroup. In a couple of weeks we will be upgrading our production server to Solaris 9 and are planning on doing a fresh install of Samba 3.0.10 at the same time. I will be saving the following files to be restored after

Hi, I am trying to construct a svmpoly model using the "caret" package (please see code below). Using the same data, without changing any setting, I am just changing the seed value. Sometimes it constructs the model successfully, and sometimes I get an ?Error in indexes[[j]] : subscript out of bounds?. For example when I set seed to 357 following code produced result only for 8

I have been trying to run a logistic regression using a number of studies. Below is the syntax, error message & data. Any advice regarding what I am doing wrong or solutions are appreciated, regards Bob Green > logreg <- read.csv("c:\\logregtest.csv",header=T) > attach(logreg) > names(logreg) [1] "medyear" "where" "who"

Hi there, I've looked through the very helpful advice about adding fitted lines to plots in the r-help archive, and can't find a post where someone has offered a solution for my specific problem. I need to plot logistic regression fits from three differently-sized data subsets on a plot of the entire dataset. A description and code are below: I have an unbalanced dataset

I really like these Trellis graphics but how do I get this code to skip the missing? logreg<-read.csv("logreg.csv", header=TRUE, sep=",", na.string=" ") attach(logreg) bwplot(yesno~bc_pcb_tot |varlist, data=logreg, main="Box Cox PCB transformation", auto.key=TRUE, fontfamily = "HersheySans" ) Dean Sonneborn M.S. Public Health Sciences *

Hi everyone I am trying to do cross validation (10 fold CV) by using e1071:svm method. I know that there is an option (?cross?) for cross validation but still I wanted to make a function to Generate cross-validation indices using pls: cvsegments method. ##################################################################### Code (at the end) Is working fine but sometime caret:confusionMatrix

Hi All, I am trying to learn Neural Networks. I found that R has packages which can help build Neural Nets - the popular one being AMORE package. Is there any book / resource available which guides us in this subject using the AMORE package? Any help will be much appreciated. Thanks, Indrajit

Dear R-users: How does one calculate in R the odds ratios for a CATEGORICAL predictor variable that has 4 levels. I see r-help inquiries regarding odds ratios for what looked like a continuous predictor variable. I was wondering how to get the pairwise odds ratios for comparisons of levels of a categorical predictor variable. I can't seem to get the correct output using: >

Hi, I was trying to plot the logistic regression from a regression "logreg" I just ran. I downloaded the "car" package from the R website and went to Packages -> install package from local zip file I checked in my library file and the package is there. I restarted R. I then ran the command: reg.line(logreg,col=palette()[2], lwd=2, lty=1) And I get the error: Error: could

Hi, I have a question on read.table. I have a dataset with 273,000 lines and 195 columns. I used the read.table to load the data into R: trn<-read.table('train1.dat', header=F, sep='|', na.strings='.') I found it takes forever. then I run 1/10 of the data (test) using read.table again. And this time it finished quickly. So, there might be something wrong in my data

Hi there: recently i try to use LogicReg package for a tree model(logistics fit ) . i list my code and error below: > dim(model.dat) [1] 48000 745 > fit1 <- logreg(resp = model.dat[,745], bin=model.dat[, 9:700], type = 3, select = 3, ntrees = c(1,2), nleaves=c(1,7), ) Insufficient declaration LGCn1MAX in logreg() is 20000 LGCn1MAX should be at least 48000 Please fix and

iterations <- 100 nvars <- 4 combined <- rbind(scaleMiceTrain, scaleMiceTest) reducedSample <- combined reducedSample <- subset(reducedSample, select = -pID50) reducedSample <- subset(reducedSample, select = -id) for (i in 1:iterations) { miceSample <- sample(combined[,-c(1,2)],nvars, replace=FALSE) miceSample$pID50 <- combined$pID50 miceTestSample <-

I want to use the grplasso package on a data set where I want to fit a linear model.? My interest is in identifying significant?beta coefficients.? The documentation is a bit cryptic so I'd appreciate some help. ? I know this is a strategy for large numbers of variables but consider a simple case for pedagogical puposes.? Say I have?two 3 category predictors (2 dummies each), a binary

Hello. I have run the command imp<-mice(mydata, im=c("","pmm","logreg","logreg"),m=5)  for a variable with no missing data, a numeric one and two variables with binary data. I got the following message: There were 37 warnings (use warnings() to see them) > warnings() Warning messages: 1: In any(predictorMatrix[j, ]) ... : coercing argument of

I want to get some "simple" logic regression examples to work before exploring a hard problem. I can get results, but I'm having some problems using "cat" to save the logic regression equation to a disk file. Consider this: # Simple Logic Regression Example # efg, 23 Feb 2005 library(LogicReg) # Create simulated data with known logic equation: # "noise"

Hi all, I have just installed OSX Server 10.4 and R comes up with the incompatible libxml library message reported by Dan Kelley a few messages ago. Xcode 2 does not ship with Tiger Server. I installed the X-Windows code. I can report that the version of libxml2.2 that is installed in this case is the version 8.0.0 dylib. [6]sboker at munimula:/usr/lib % ls -l libxml2.2* -rwxr-xr-x 1

When I import a simple dataset, run LDA, and then try to use the model to forecast out of sample data, I get a forecast for the training set not the out of sample set. Others have posted this question, but I do not see the answers to their posts. Here is some sample data: Date Names v1 v2 v3 c1 1/31/2009 Name1 0.714472361 0.902552278 0.783353694 a 1/31/2009 Name2 0.512158919 0.770451596

No, no, these will not implement Fermi reclocking. This set of patches contains some of the preparatory work that I deem stable enough to move upstream. Notable changes - Training pattern upload routines from GK104+ now shared with GT215+ - Timing calculation for Fermi - GDDR5 MR calculation from VBIOS timing table v1.0. Also useful for that pesky GT 240. - A routine to translate a VBIOS init