similar to: Plot of odds ratios obtained from a logistic model

Hi, I'm using the rms package to do regression analysis using the lrm function. Retrieving odds ratios is possible using summary.rms. However, I could not find any information on how exactly the odds ratios for continuous variables are calculated. It doesn't appear to be the odds ratio at 1 unit increase, because the output of summary.rms did not match the coefficient's value. E.g.

The code library(Design) f <- lrm(y~x1+x2+x1*x2, data=data) plot(f) produces a plot of log odds vs x2 with 0.95 confidence intervals. How do I get a plot of odds ratios vs x2 instead? Thanks -- View this message in context: http://r.789695.n4.nabble.com/Odds-ratios-from-lrm-plot-tp4033340p4033340.html Sent from the R help mailing list archive at Nabble.com.

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 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 all, I am trying to fit a logistic regression for a bivariate response using five independent variables in a stepwise procedure. My outputs look okay but does any one know (or is there any literature on) how the confidence intervals are calculated for the reported odds ratios..? Thanks! [[alternative HTML version deleted]]

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')

Hi, I am a long time SPSS user but new to R, so please bear with me if my questions seem to be too basic for you guys. I am trying to figure out how to analyze survey data using logistic regression with multiple imputation. I have a survey data of about 200,000 cases and I am trying to predict the odds ratio of a dependent variable using 6 categorical independent variables (dummy-coded).

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

Hi, I am trying to construct a logistic regression model from my data (104 patients and 25 events). I build a full model consisting of five predictors with the use of penalization by rms package (lrm, pentrace etc) because of events per variable issue. Then, I tried to approximate the full model by step-down technique predicting L from all of the componet variables using ordinary least squares

Dear R-Helpers, I am a novice in survival analysis. I have the following code: for (i in 3:12) print(coxph(Surv(time, status)~a[,i], data=a)) I used it to fit the Cox Proportional Hazard models separately for every available parameter (columns 3:12) in my data set - with intention to compare the Hazard Ratios. However, some of my variables are in range 0.1 to 1.6, others in range 5000 to

I am a new R user (am using it through the Rcmdr package) and have struggled to find out how to report OR and RR directly when running GLM models (not only reporting coefficients.) Example of the syntax that I have used: GLM.2 <- glm(diarsev ~ treatmentarm +childage +breastfed, family=binomial(logit), data=fieldtrials2) summary(GLM.2) This works well except that I manually have to calculate

lrm does some binning to make the calculations faster. The exact calculation is obtained by running f <- lrm(...) rcorr.cens(predict(f), DA), which results in: C Index Dxy S.D. n missing 0.96814404 0.93628809 0.03808336 32.00000000 0.00000000 uncensored Relevant Pairs Concordant Uncertain 32.00000000

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,

Does anyone know of another version of the Mcnemar test that provides: 1. Odds Ratios 2. 95% Confidence intervals of the Odds Ratios 3. Sample probability 4. 95% Confidence intervals of the sample probability Obviously the Odds Ratios and Sample probabilities are easy to calculate from the contingency table, but I would appreciate any help on how to calculate the confidence

Dear All, I am learning the ropes about logistic regression in R. I found some interesting examples http://bit.ly/Vq4GgX http://bit.ly/W9fUTg http://bit.ly/UfK73e but I am a bit lost. I have several questions. 1) For instance, what is the difference between glm.out = glm(response ~ poverty + gender, family=binomial(logit), data=mydata) and glm.out = glm(response ~ poverty * gender,

Hi there, is there any way to compare 2 odds ratios? I have two tests that are supposed to detect a disease presence. So for each test, I can compute an odds ratio. My problem is how can I compare the 2 tests by testing whether the 2 odds ratios are the same? Appreciate

Hi, does anyone know of a function to calculate odds ratios in multiway tables (stratified) (+ the other usual statistics involved) i mean: say we have a table r*c*d, For every d (depth) we have a r*c table, and in this table the odds ratio's are calculated for every 2*2 subtable in it. logically this function would look like): ORs(multiwaytable) or ORs(data$var1r,data$var2c,data$var3d)

Hi, I have the following table of odds ratios (or), lower limits(ll) and upper limits(ul), which I would like to plot as horizontal lines beginning at the lower limit, ending at the upper limits and with a dot at the odds ratio on an x-axis on a log10 scale. The y axis would be the study sites. >From what I can figure out, it looks like the plotCI function will do everything except give me an

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 everyone, I'm doing a logistic regression with an ordinal variable. I'd like to set the contrasts on the ordinal variable. However, when I set the contrasts, they work for ordinary linear regression (lm), but not logistic regression (lrm): ddist = datadist(bin.time, exp.loc) options(datadist='ddist') contrasts(exp.loc) = contr.treatment(3, base = 3, contrasts = TRUE) lrm.loc =