similar to: logistic regression with 50 varaibales

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Dear All, Suppose that you are trying to create a binary logistic model by trying different combinations of predictors. Has R got an automatic way of doing this, i.e., is there some way of automatically generating different tentative models and checking their corresponding AIC value? If so, could you please direct me to an example? Thanks in advance, Paul

Dear list, first accept my apologies for asking a non-R question. Can anyone point me to a good reference on logistic regression? web or book references would be great. I am interested in the use and interpretation of dummy variables and prediction models. I checked the contributed section in the CRAN homepage but could not find anything (Julian Faraway?s "practical Regression and ANOVA

Hi, I understand that dichotimization of the predicted probabilities after logistic regression is philosophically questionable, throwing out information, etc. But I want to do it anyway. I'd like to include as a measure of fit % of observations correctly classified because it's measured in units that non-statisticians can understand more easily than area under the ROC curve, Dxy, etc.

Hi, R users, Is there any package for penalized logistic regression with more than two response classes? I read the manual for stepPlr, but it seems it's only for binary case. Thank you, Annie [[alternative HTML version deleted]]

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

Hello - I apologize if this question is simple/obvious, but I couldn't find a satisfactory answer online, and I am not very accustomed to working with R (Matlab is my poison. :-)). Any help would be greatly appreciated. I have a model with a three-level factor and a continuous covariate. The call I use is: mymodel <- glm(Response ~ Factor_covariate + continuous_covariate - 1,

Hi, R users, What may be the best function in R to do variable selection in logistic regression? I have the same number of variables as the number of samples, and I want to select the best variablesfor prediction. Is there any function doing forward selection followed by backward elimination in stepwise logistic regression? Thanks, Annie [[alternative HTML version deleted]]

Does anyone know of any X^2 tests to compare the fit of logistic models which factor out the sample size? I'm dealing with a very large sample and I fear the significant X^2 test I get when adding a variable to the model is simply a result of the sample size (>200,000 cases). I'd rather use the whole dataset instead of taking (small) random samples as it is highly skewed. I've

I am interested in knowing whether and how I can test the significance of the relationship between my continuous predictor variable (a covariate) and my binary response variable according to two different groups, my categorical predictor variable, in a logistic regression model (glm). Specifically, can I determine whether the relationships are identical (the hypothesis of coincidence), or whether

Hi, Can one tell me which procedure will fit an ordinal logistic regression model for longitudinal data set. To be precise, I have both dichotomous and polytomous items. Also, I would like to specify different covariance structures (unstructured, ar1 etc) for trial runs. Thanks -- View this message in context:

Hi, I have a dataset where the response for each person on one of the 2 treatments was a proportion (percentage of certain number of markers being positive), I also have the number of positive & negative markers available for each person. what is the best way to analyze this kind of data? I can think of analyzing this data using glm() with the attached dataset:

I do not see an R-squared value after preforming the glm regression. Is there a separate command for this? Thanks -- View this message in context: http://r.789695.n4.nabble.com/Obtaining-R-squared-value-in-Logistic-Regression-tp4648954.html Sent from the R help mailing list archive at Nabble.com.

Hello, I am wondering if R has any ways to conduct the score test in logistic regression? Could you let me know please? Thanks, Insu -------------------------------------------------- This e-mail and any files transmitted with it may contain privileged or confidential information. It is solely for use by the individual for whom it is intended, even if addressed incorrectly. If you

Dear People at R help, I am trying to figure out the syntax for a three-level logistic model with a single random effect (intercept): Data Collected My data consist of three levels: level 1 is four setting for each student (setting nested within student), and each student is registered in one of 14 universities (students nested within university). More detailed: A. 2,479 students who have a

Hi, I have a dichotomous variable (Q1) whose answers are Yes or No. Also I have 2 categorical explanatory variables (V1 and V2) with two levels each. I used logistic regression to determine whether there is an effect of V1, V2 or an interaction between them. I used the R and SAS, just for the conference. It happens that there is disagreement about the effect of the explanatory variables

Dear all, I want to do a logistic regression. So far I've only found out how, in a dataset of complete cases. I'd like to do logistic regression via max likelihood, using all the study cases (complete and incomplete). Can you help? I'm using glm() with family=binomial(logit). If any covariate in a study case is missing then the study case is dropped, i.e. it is doing a complete case

Dear Masters, first I'd like to wish u all a great 2011 and happy holydays by now, second (here it come the boring stuff) I have a question to which I hope u would answer: I run a logistic regression by glm(), on the following data type (y1=1,x1=x1); (y2=0,x2=x2);......(yn=0,xn=xn), where the response (y) is abinary outcome on 0,1 amd x is any explanatory variable (continuous or not)

Hi all, I am running Stepwise logistic regression and i have : 1- Multiple covatiates included in each model (No missing data) 2- Genotype data (SNPs) about 500,000 . I partitioned the data to multiple files (there are missing data) I run the step by including all the covariates and one SNP at each model. but i got this message : number of rows in use has changed: remove missing values? In

Hi, I'm developing an experiment with logistic regression. I've come across the lrm function in the Design library. While I understand and can use the basic functionality, there are a ton of options that go beyond my knowledge. I've carefully read the help page for lrm, but don't understand many of the arguments and optional return values. (penalty, penalty.matrix,