similar to: correlated predictors in lr

I'm using the package 'lars' in R with the following code: > library(lars) > set.seed(3) > n <- 1000 > x1 <- rnorm(n) > x2 <- x1+rnorm(n)*0.5 > x3 <- rnorm(n) > x4 <- rnorm(n) > x5 <- rexp(n) > y <- 5*x1 + 4*x2 + 2*x3 + 7*x4 + rnorm(n) > x <- cbind(x1,x2,x3,x4,x5) > cor(cbind(y,x)) y x1 x2

Hi, I would like to simulate data with a binary outcome and a set of predictors that are correlated. I want to be able to fix the number of event (Y=1) vs. non-event (Y=0). Thus, I fix this and then simulate the predictors. I have 2 questions: 1. When the predictors are continuous, I can use mvrnorm(). However, if I have continuous, ordinal and binary predictors, I'm not sure how to simulate

Dear R users, I’m trying to understand how correlated predictors impact the Relative Importance measure in Stochastic Boosting Trees (J. Friedman). As Friedman described “ …with single decision trees (referring to Brieman’s CART algorithm), the relative importance measure is augmented by a strategy involving surrogate splits intended to uncover the masking of influential variables by others

URIS http://rubyforge.org/frs/?group_id=1024 http://www.codeforpeople.com/lib/ruby/ SYNOPSIS open child process with handles on pid, stdin, stdout, and stderr: manage child processes and their io handles easily. HISTORY 0.5.1: - fixes a __critical__ but in ThreadEnsemble class that had a race condition that could cause thread deadlock. sorry bout that folks.

URIS http://rubyforge.org/projects/codeforpeople/ http://www.codeforpeople.com/lib/ruby/ SYNOPSIS open child process with handles on pid, stdin, stdout, and stderr: manage child processes and their io handles easily. HISTORY 0.5.0: - on the suggestion of tim pease (thanks tim!), i added timeout features to open4. the command run may have an overall timeout and

hello vorlon, got notified of your patch, will apply next days upstream unless some critiques are voiced on ml. thanks. -- maks ----- Forwarded message from Steve Langasek <steve.langasek at canonical.com> ----- Date: Wed, 16 Feb 2011 22:05:42 -0000 From: Steve Langasek <steve.langasek at canonical.com> Subject: [Bug 527720] Re: thumb2 porting issues identified: klibc uses

If one estimates a model using multinom, is it possible to perform the omnibus LR test ( the analogue to omnibus F in linear models ) using the output from multinom ? The residual deviance is there but I was hoping I could somehow pull out the deviance based on just using an intercept ? Sample code is below from the CAR book but I wasn't sure how to do it based on that example. Thanks

when i try to validate my logistic regression model: fit<-glm(y~x,binomial,data=dataname,x=TRUE,y=TRUE) validate(fit,method="boot",B=150,...) i get the following error message: Error in UseMethod("validate") : no applicable method for "validate" any insight would be appreciated. many thanks! -- View this message in context:

Is there any way in R to do a linear regression with positive coefficients only? Thanks for any help! Sandor Lehoczky -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To:

Hi I'm encountering some problems with coxme My data: I'm looking at the survival of animals in an experiment with 3 treatments, which came from 4 different populations, two of which were infected with a parasite and two of which were not. I'm interested if infected animals differe from uninfected ones across treatments. Factor 1: treatment (3 levels) Factor 2: infection state

Dear all, I don't get how llvm handles functions with __attribute__((noreturn)). It seems that LR register is backed up on the stack whilst it will never be used to return from a 'noreturn' function. I have this problem with a home-made backend but it seems that ARM flavour of clang has same behaviour. By the way, SP is also saved, I don't understand why. Is there a syntax error

I have numerous replicated goodness-of-fit experiments (observed compared to expected counts in categories) and these replicates are nested within a factor. The expected counts in each cell are external (from a scientific model being tested). The calculations I need within each level of the nesting factor are a heterogeneity G test, with the total G and the pooled G across replicates. Then I

We are using glm() to models to counts of deaths due to rare causes using a log link and Poisson error distribution, with population as the offset. Approximate confidence intervals for the parameter estimates are easy to calculate using a standard normal deviate, but obviously when the counts of deaths are small (which is why we are using Poisson regression), these intervals are very approximate

InDATA <-read.table("C:/Data/May 2004/season.txt",header=T) X <- decompose(InDATA) print(X) Period Connections Q1 67519 Q2 69713 Q3 68920 Q4 69452 Q1 70015 Q2 59273 Q3 57063 Q4 65596 Q1 73527 Q2 58586 Q3 69522 Q4 60091 Q1 51686 Q2 63490 Q3 55702 Q4 53200 Q1 51033 Q2 48175 Q3 52709 Q4 50106 Q1 50855 Q2 43466 Q3 48190 Q4 41702 Q1 48747 Q2 51441 Q3 42537

Hello Group, This is an open-ended question. Quite fascinated by the things I can do and the control I have on my activities since I started using R. I basically have been using this for analytical related work off my desktop. My experience has been quite good and most issues where I need to investigate and solve are typical items more related to data errors, format corruption, etc... not

During context switch is_running is set for the next vcpu before the gic state is actually saved. This leads to possible nasty races when interrupts need to be injected after is_running is set to the next vcpu but before the currently running gic state has been saved from the previous vcpu. Use current instead of is_running to check which one is the currently running vcpu: set_current is called

Hi R-people, I have a question about lattice in general, and histogram specfically. How do you control the ordering of factors that controls the placement of the conditional panels. I have a dataset with factors that go 'Q1','Q2',"Q3','Q5' and of course I want the plot to place Question Q1 at the top and Question Q5 at the bottom of the graphical output.

Hello Everyone, Trying to learn a little bit about data mining. I'm working on a text mining project that will attempt to predict whether cancer patients got a particular type of genetic testing. A subsequent stage then will be aimed at predicting what the results of that testing were. ? I've used the tm package to prepare my data and am planning to use rattle to do the actual data

Hi Guys, I'm using zoo package now. I found lag is not doing what I assumed. > x <- zoo(11:21) > z <- zoo(1:10, yearqtr(seq(1959.25, 1961.5, by = 0.25)), frequency = 4) > x 1 2 3 4 5 6 7 8 9 10 11 11 12 13 14 15 16 17 18 19 20 21 > lag(x) 1 2 3 4 5 6 7 8 9 10 12 13 14 15 16 17 18 19 20 21 > z 1959 Q2 1959 Q3 1959 Q4 1960 Q1 1960 Q2 1960 Q3 1960 Q4

I have a data.frame named "df". The dput of df is at the bottom of this e-mail. What I'd like to do is replace the "n/a " values with NA. On Mac OSX, it works to do this: df[df == "n/a"] <- NA However, it does not work on Ubuntu. See below. Thanks in advance, Garrett > x <- df[27, 4] # complete data.frame dput is below > dput(x) "n/a?"