similar to: help with formula for clogit

Hello gurus, I have some variables, and i am creating combinations for analysis in the end i need these variables to be displayed like "LEPTIN+SAA+PTH". currently i am using loop to perform this. I would appreciate any pointers to do it without the loop. > mols=c("LEPTIN","SAA","PTH","sEGFR") > samples=mols[1:3] > samples [1]

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 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+

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

Hi All, I'm trying to run a conditional logistic regression in R (2.14.0) using clogit from the survival package. The dataset I have is relatively small (300 observations) with 25 matched strata- there are roughly 2 controls for each case, and some strata have multiple case/control groups. When I try to fit a very simple model with a binary outcome and a single continuous exposure R seems to

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

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

I have a data frame like this xx<-data.frame(cbind(Sample=c('Ctrl_6h','1+0_6h','1+200_6h','1+5k_6h','Ctrl_5K_6h','ConA_6h'), IFIT1=c(24,25,24.7,24.5,24.2,24.8))) grep('[[:digit:]]h',xx$Sample) yy<-xx$Sample strsplit(yy,"_") I have to extract the time information separated by '_' in the sample

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 <-

-----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

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

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

--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

so here is the code I need help to fix the line for 'st' when it reaches maximum, that's where i am getting stuck. thanks Sharad 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) r2=c() cinit=0 for (col in 1:9) { cinit=cinit+1 st=cinit+1

Hello R gurus, I have the code below for which i need help and pointers to make it run in parallel on a dual core win7 computer with R 2.13.x, using foreach, iterators,doMC. library(scatterplot3d) # Loads 3D library. library(fields) library(MASS) library(ROCR) library(verification) library(caret) library(gregmisc) ##simulated data d=replicate(9, rnorm(40)+10)

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 am analyzing a 1-to-2 matched case-control study using clogit in the survival package. I am interested in comparing and assessing fit of non-nested models. I don't want to program all the diagnostics described in Hosmer/Lemeshow (2000). Can someone proficient with clogit and assessing fit for non-nested models point me in the right direction. Many thanks! Tomas Tomas Aragon, MD, DrPH Tel:

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.