similar to: variable selection in logistic

Hi All, How can I get the expected value from a discrete cdf? Is there any R function that can do this? Thanks, Annie [[alternative HTML version deleted]]

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, All, How can I get the indices of the minimum elements in a matrix without using a loop? For example, if the matrix is 4 5 2 2 8 9 5 2 3 Then I want to output (1,3), (2,1), (3,2). Thanks, Annie [[alternative HTML version deleted]]

Hi, R users, I am trying to use glm to do logistic regression. I know generally when I have two covariates, say x1 and x2, then I do fit <- glm(y~x1+x2,famliy='binomial') But now my covariates form a n*p matrix, say x, so actually each column is a covariate. So I think I should do fit <- glm(y~x,family='binomial') Then I need to predict new data. How should I write the

Hi All, I have a 2-dim density defined on 0<x<1, 0<y<1, x<y. I know the exact formula of the density. How can I visualize it? What plot functions can I use? Thanks, Annie [[alternative HTML version deleted]]

 Hi, Is there any function available to do stepwise selection of variables in Conditional(matched) logistic regression( clogit)? step, stepwise  etc are failing in case of conditional logistic regression. Please help.  Thanks P.T. Subha [[alternative HTML version deleted]]

Hi, Is there any package for logistic model selection using BIC and Mallow's Cp statistic? If not, then kindly suggest me some ways to deal with these problems. Thanks. -- View this message in context: http://www.nabble.com/Stepwise-logistic-model-selection-using-Cp-and-BIC-criteria-tf4464430.html#a12729613 Sent from the R help mailing list archive at Nabble.com.

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

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In a generalized linear model with k covariates, there are 2(kth power) - 1 possible models (excluding interactions). Awhile ago a posting to R-help suggested Model Selection and Multimodel Inference, 2nd ed, by Burnham and Anderson as a good source for understanding model selection. They recommend (page 71) computing AIC differences over all candidate models in the set of possible models. After

I am creating habitat selection models for caribou and other species with data collected from GPS collars. In my current situation the radio-collars recorded the locations of 30 caribou every 6 hours. I am then comparing resources used at caribou locations to random locations using logistic regression (standard habitat analysis). The data is therefore highly autocorrelated and this causes Type

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]]

Sorry, I have wrote a wrong subject in the first email! Regards, Linda ---------- Forwarded message ---------- From: linda Porz <linda.porz@gmail.com> Date: 2011/5/25 Subject: combined odds ratio To: r-help@r-project.org Cc: r-help-request@stat.math.ethz.ch Dear all, I am looking for an R function which does stepwise selection cox model in r (delta chisq likelihood ratio test) similar

Hello useRs, I have a problem at hand which I'd think is fairly common amongst groups were R is being adopted for Analytics in place of SAS. Users would like to obtain results for logistic regression in R that they have become accustomed to in SAS. Towards this end, I was able to propose the Design package in R which contains many functions to extract the various metrics that SAS reports.

Dear R-help members, I'd like to run a binomial logistic stepwise regression with ten explanatory variables and as many interaction terms as R can handle. I'll come up with the right R command sooner or later, but my real question is whether and how the criterion for the evaluation of the different models can be set to be the probability of the residual deviance in the Chi-Square

Hello, Does logistic regression really provide better results than lda or qda ? (my purpose is not classification but highlighting of discriminant variables). If this is true, where could I get an R script performing stepwise logistic regression ? Thanks -- Daniel AMORESE Lab. M2C "Morphodynamique Continentale et C?ti?re" UMR CNRS 6143 Caen/Rouen Centre de G?omorphologie UCBN

When counting required slots for skb, netback directly uses DIV_ROUND_UP to get slots required by header data. This is wrong when offset in the page of header data is not zero, and is also inconsistent with following calculation for required slot in netbk_gop_skb. In netbk_gop_skb, required slots are calculated based on offset and len in page of header data. It is possible that required slots

Hi, Does R support any of the 3 stepwise or 8 criteria methods for logistic regression and multiple linear regression that SAS supports? If yes, could you give me some simple demostration code. Thanks, steve __________________________________________________ Do You Yahoo!? Make international calls for as low as $.04/minute with Yahoo! Messenger http://phonecard.yahoo.com/

Hi, When I use logistic regression, each variable has a p value associated with it. Do I only include the variables that have a statistically significant p value (<0.05), or are there situations when I should include variables when their p values are high? I had heard that if a variable has a high p value but it's not the terminal variable, keep it; otherwise, take it out. Not sure if

Thank you Frank and all for your advices. Here I attach the raw data from the Pawinski's paper. I have obtained permission from the corresponding Author to post it here for everyone. The only condition of use is that the Authors retain ownership of the data, and any publication resulting from these data must be managed by them. The dataset is composed as follows: patient number / MMF dose in