similar to: need help in logistic regression

Hi guys, I have 1000 rows of a dataset. In my analysis, I need 70% of the data, run my analysis and then use the remaining 30% to test my model. Could anybody kindly help me on this? Cheers

Hi guys, This is a very beginner question. Anybody willing to help? for(i in 1:1000) x=29.5 + i/500 y=2x plot(y,x) The idea is to produce 1000 values of x and y then plot them. Cheers, Eddie [[alternative HTML version deleted]]

On Wed, May 29, 2013 at 03:25:30PM +0200, Duncan Sands wrote: > Hi Jack, I pulled the loop vectorizer and fast math changes into the 3.3 branch, > so hopefully they will be part of 3.3 rc3 (and 3.3 final!). It would be great > if you could redo the benchmarks rc3. > Duncan, As requested, appended are the updated Polyhedron 2005 benchmark results with both RC1 and RC3 llvm 3.3

Dear list: After reading the following two links: http://luna.cas.usf.edu/~mbrannic/files/regression/Logistic.html http://www.tufts.edu/~gdallal/logistic.htm I've known the mathematical basis for logistic regression.However I am still not so sure about the "logit " For a categorical independent variable, It is easy to understand the procedures how "log

Dear R-helpers I have a question related to logistic GAM models. Consider the following example: # Load package library(mgcv) # Simulation of dataset n <- 1000 set.seed(0) age <- rnorm(n, 50, 10) blood.pressure <- rnorm(n, 120, 15) cholesterol <- rnorm(n, 200, 25) sex <- factor(sample(c('female','male'), n,TRUE)) L <-

Hi, I am trying to run a simple logistic regression using lrm() to calculate a odds ratio. I found a confusing output when I use summary() on the fit object which gave some OR that is totally different from simply taking exp(coefficient), see below: > dat<-read.table("dat.txt",sep='\t',header=T,row.names=NULL) > d<-datadist(dat) > options(datadist='d')

R-experts: I would like to explore the partial proportional odds models of Peterson and Harrell (Applied Statistics 1990, 39(2): 205-217) for a dataset that I am analyzing. I have not been able to locate a R package that implements these models. Is anyone aware of existing R functions, packages, etc... that might be used to implement the partial proportional odds models? Brant Inman

Sorry if this is a duplicate: This is a re-post because the pdf's mentioned below did not go through. Hello, I'm new'ish to R, and very new to glm. I've read a lot about my issue: Warning message: glm.fit: fitted probabilities numerically 0 or 1 occurred ...including: http://tolstoy.newcastle.edu.au/R/help/05/07/7759.html

had to fix the subject... was getting under my skin! yeah, could you put up the FLAC version of the worst track that is less than 20 megs compressed? (I'll have to grab it with a 56k modem). by worst I mean the one where shorten beats flac by the most. also: 1. what version of shorten are you using? 2. what command-line options for flac and shorten did you use on this track? thanks, Josh

Hi, I am new to R. Anyone can explain the following from R-help or anyone can direct me how to calculate odds ratio from logistic model in R. Thank you very much. Guoya Stefano <stecalza at tiscalinet.it <https://stat.ethz.ch/mailman/listinfo/r-help> > writes: >Hi all. > >A simple question. >Is there a function to compute the Odds Ratio and its confidence intervall,

Dear all, I am looking for ways to compute standardized logistic regression coefficients. I found papers describing at least 6 different ways to standardize logistic regression coefficients. I also found a very old (Thu May 12 21:50:36 CEST 2005) suggestion by Frank E Harrell (one of the colleagues who frequently contribute on this list) saying... Design doesn't implement those because they

Hello all. I am doing one part of an evaluation of a mandatory welfare-to-work programme in the UK. As with all evaluations, the problem is to determine what would have happened if the initiative had not taken place. In our case, we have a number of pilot areas and no possibility of random assignment. Therefore we have been given control areas. My problem is to select for survey individuals in

Hi, I am doing a retrospective analysis on a cohort from a designed trial, and I am fitting the model fit<-glmD(survived ~ Covariate*Therapy + confounder,myDat,X=TRUE, Y=TRUE, family=binomial()) My covariate has three levels ("A","B" and "C") and therapy has two (treated and control), confounder is a continuous variable. Also patients were randomized to

I've been using R to do logistic regresssion, and that's working well, but there are two things I haven't figured out how to do. (1) Is there some pre-existing function that will let you compute the odds ratios and confidence intervals for them for a specific fit. I know how to do this manually or even write a function that I can call with the coefficients and se, but

I am trying to do logistic regression with a categorical predictor variable with the glm() function, family=binomial. Using glm() I would like to be able to calculate the confidence intervals of all three possible odds ratios for a factor (the factor has three categories). Three categories imply two columns of 0's and 1's in the design matrix, and two parameter estimates with their

Dear gurus, I've analyzed a (fake) data set ("data") using logistic regression (glm): logreg1 <- glm(z ~ x1 + x2 + y, data=data, family=binomial("logit"), na.action=na.pass) Then, I created a data frame with 2 fixed levels (0 and 1) for each predictor: attach(data) x1<-c(0,1) x2<-c(0,1) y<-c(0,1) newdata1<-data.frame(expand.grid(x1,x2,y))

Hi R help list I'm simulating logistic regression data with a specified odds ratio (beta) and have a problem/unexpected behaviour that occurs. The datasets includes a lognormal exposure and diseased and healthy subjects. Here is my loop: ors <- vector() for(i in 1:200){ # First, I create a vector with a lognormally distributed exposure: n <- 10000 # number of study subjects

Currently, Xen does not support injecting an NMI to HVM guest OS. Adding this feature is necessary for those softwares which depend on NMI to function correctly, such as KDB and oprofile. The attached patch allows NMI to be injected to guest OS in NMIP capable platforms. It also enables to queue an NMI and then inject it as soon as possible. Signed-off-by: Haitao Shan

Hi, I am using logistic regression to classify a binary psychometric data. using glm() and then predict.glm() i got the predicted odds ratio of the testing data. Next i am going to plot ROC curve for the analysis of my study. Now what i will do: 1. first select a cut-off (say 0.4) and classify the output of predict.glm() into {0,1} segment and then use it to draw ROC curve using ROCR package

(Sorry for obvious mistakes, as I am quite a newby with no Statistics background). My question is going to be what is the gain of logistic regression over odds ratios when none of the input variables is continuous. My experiment: Outcome: ordinal scale, ``quality'' (QUA=1,2,3) Predictors: ``segment'' (SEG) and ``stress'' (STR). SEG is nominal scale with 24