similar to: Processing time on clogit

Hi, I am playing with clogit(case~spontaneous+induced+strata(stratum),data=infert) from clogit help file. This line works. 1. But, why strata(stratum) doesn't have a coefficient like spontaneous and induced? 2. When I remove strata(stratum) from the command, this function seems to keep running forever. Why? 3. I think the equation for clogit looks like P=1/(1+

I would like to know if clogit function can be used as below clogit(group~., data=dataframe) When I try to use in above format it takes a long time, I would appreciate some pointers to get multiple combinations tested. set.seed(100) d=data.frame(x=rnorm(20)+5, x1=rnorm(20)+5, x2=rnorm(20)+5, x3=rnorm(20)+5, x4=rnorm(20)+5, x5=rnorm(20)+5, x6=rnorm(20)+5, x7=rnorm(20)+5, x8=rnorm(20)+5,

Dear R users: I am not entirely convinced that clogit gives me the correct result when I use pspline() and maybe you could help correct me here. When I add a constant to my covariate I expect only the intercept to change, but not the coefficients. This is true (in clogit) when I assume a linear in the logit model, but the same does not happen when I use pspline(). If I did something similar

Can someone clarify what I cannot make out from the documentation? The function 'clogit' in the 'survival' package is described as performing a "conditional logistic regression". Its return value is stated to be "an object of class clogit which is a wrapper for a coxph object." This suggests that its usefulness is confined to the sort of data which arise in

Hi, I'm calculating a conditional logit on some data stratified by group. My understanding was that a conditional logit by definition returns a value between 0 and 1 a a probability. Can anyone suggest why I'm seeing results outside of the {0,1} range?? The call in R is: m <- clogit(score ~ val_1 + val_2 + strata(group), data=data) Then prediction <- predict(m,newdata) A

How can I get the log odds associated with the levels in strata() within a clogit() model? I'm running R-1.9.0 on a Linux platform. I am using clogit() to run a Rasch model in Item Response Theory in psychometrics. Symbolically, the model is: logit(p_{j,k}) = \log({\Pr(p_{j,k}) \over \Pr(1-p_{j,k})}) = \theta_j - \alpha_k, That is, the log odds of answering an test item correctly is

Nelly Reduan a partag? un fichier OneDrive avec vous. Pour l?afficher, cliquez sur le lien ci-dessous. <https://1drv.ms/u/s!Apkg2VlgfYyDgRAeVIM0nEajx0Fb> [https://r1.res.office365.com/owa/prem/images/dc-png_20.png]<https://1drv.ms/u/s!Apkg2VlgfYyDgRAeVIM0nEajx0Fb> Screenshot 2017-11-12 18.49.43.png<https://1drv.ms/u/s!Apkg2VlgfYyDgRAeVIM0nEajx0Fb> Hello How can I perform

--begin inclusion -- I have a matched-case control dataset that I'm using conditional logistic regression (clogit in survival) to analyze. I'm trying to conduct k-folds cross validation on my top models but all of the packages I can find (CVbinary in DAAG, KVX) won't work with clogit models. Is there any easy way to do this in R? -end inclusion -- The clogit funciton is simply a

Hi Folks, Hopefully this question has enough R and not too much stats to be appropriate for this list. Based on,* Hosmer et al. 2013. Logistic regression for matched case-control studies. Applied Logistic Regression *(eqtn. 7.8)*, *I am assessing GOF of conditional (or matched) logistic regression models with the *standardized Pearson residuals*. The authors define ?large? as delta chi-squared

Dear All: I have been struggling to run the conditional logistic regression on a dataset. I am using clogit function in survival package. Here is how the data is generated. I tried to shorted the data by using the second count variable but still the clogit did not run. Why I am not able to run clogit on my dataset. I appreciate any input. Regards, Ashraf age <- c(rep(0,8),rep(1,8)) scc <-

Hello, I am using the clogit (conditional logistic regression) on a simple data set which is not related to survivorship, which I understand to be fine. I have trouble understanding the output. I would like to find parameter estimates of the logistic models I am constructing with R, and am unclear as to what the coefficients represent that are provided. The below is a copy of my output.

I have a case-control study that I'm analysing using the conditional logistic regression function clogit from the survival package. I would like to calculate the AICc of the models I fit using clogit. I have a variety of scripts that can calculate AICc for models with a logLik method, but clogit does not appear to use this method. Is there a way I can calculate AICc from clogit in R? Many

Dear R-Help, I wonder if there is a prediction function for a clogit model which can be used in the same way as the predict function for the multinom model. In prediction('multinommodel',testset ...) it is possible to predict the class or the class probabilities for a testset. There is a predict function for the coxph model but I cannot find an way to use this to predict the classes

Hello all I'm moving back and forth between stata and R at the moment - of course, using R whenever possible :-) I'm running conditional logits on some panel data and I get slightly different results and different N in the two programs. In R I run clogit(trans.dem ~ I(avg.gle_rgdp.500/gle_rgdp) + log(gle_rgdp) + timesince.dem + I(timesince.dem^2) + timesince.dict + I(timesince.dict^2) +

I still don't think the exp(lp)/(1+exp(lp)) gonna work. Since this is conditional logit model, while this formula is only used in unconditional ones. By using this, one neglects the information based on stratum. Though I don't know how to solve it to. I am also working on a project on this and I do hope there's someone explaining this problem. Will that be a possibility that the phat

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 Conditional logistic regression and small sample sizes: what to do? Dear R-Gurus, [I run R 2.8.0 on WinXP. I have no formal training in statistics.] Please feel free to skip the 'Blah blah part' for questions below. - ---Blah blah part-------- Why I use clogit (package 'survival')?: I am looking for risk factors for cases during a

Dear R-help, I'm doing conditional logit regression for a discrete choice model. I want to know whether there's a way to get the fitted probabilities. In Stata, "predict" works for clogit, but it seems that in R "predict" does not. Thank you very much! Best wishes. Sincerely, Min -- Min Chen Graduate Student Department of Agricultural,

Hi All, Does the step function work in this model? I tried to run the following model but no result obtained. The computer is hanging and I killed the job several times. Below is the code. library(survival) m.fit=clogit(y~x1+x2+x3+x4, data=ftest) summary(m.fit) final<- step(m.fit) Thanks in advance.

Dear R-helpers, I am very new to R and trying to run the conditional logit model using "clogit " command. I have more than 4000 observations in my dataset and try to predict the dependent variable from 14 independent variables. My command is as follows clmtest1 <- clogit(Pin~Income+Bus+Pop+Urbpro+Health+Student+Grad+NE+NW+NCC+SCC+CH+SE+MRD+strata(IDD),data=clmdata) However, it

Hi, I am wondering if there is a package for doing conditional logistic regression for nested case-control study as described in "Estimation of absolute risk from nested case-control data" by Langholz and Borgan (1997) where Horvitz-Thompson sampling weight (log of (number in the risk set divided by the number sampled)) is used with regression. In SAS Proc Phreg, this is implemented