similar to: clogit and general conditional logistic regression

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

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+

Hello I'm trying to solve this problem without using a for loop but I have so far failed to find a solution. I have two matrices of K columns each, e.g. (K=5), and with numbers of row N_A and N_B respectively A = (1 5 3 8 15; 2 7 20 11 13; 12 19 20 21 43) B = (2 6 30 8 16; 3 8 19 11 13) (the actual matrices have hundreds of thousands of entry, that's why I'm keen to avoid

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

--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

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

I would like to code the following in R: a1(b1+b2+b3) + a2(b1+b3+b4) + a3(b1+b2+b4) + a4(b1+b2+b3) or in summation notation: sum_{i=1, j\neq i}^{4} a_i * b_i I realise this is the same as: sum_{i=1, j=1}^{4} a_i * b_i - sum_{i=j} a_i * b_i would appreciate some help. Thank you. -- View this message in context: http://r.789695.n4.nabble.com/summation-coding-tp4646678.html Sent from the R

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

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,

Hello, I'm very new to using R, but I was told it could do what I want. I'm not sure how best to enter the information but here goes... I'm trying to transfer the following integral into R to solve for ln(gamma_1), on the left, for multiple instances of gamma_i and variable N_i. gamma_i is, for example, (0, 0.03012048, 0.05000000, 0.19200000, 0.44000000, 0.62566845) N_i (N_1 or

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, 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 am interested in whether the slopes in a linear model are different from 0. I.e. I would like to obtain the slope estimates, and their standard errors, ``relative to 0'' for each group, rather than relative to some baseline. Explicitly I would like to write/represent the model as y = a_i + b_i*x + E i = 1, ..., K, where x is a continuous variate and i indexes groups (levels of a

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

Hi all, I am doing some experiment studies... It seems to me that with different combination of 5 parameters, the end results ultimately converged to two scalars. That's to say, some combinations of the 5 parameters lead to one end result and some other combinations of the 5 parameters lead to the other end result (scalar). I am thinking of this is sort of something like clustering or

 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]]

I've put two packages for R on my home page at http://www.xs4all.nl/~jhckx/R/. The "pcnt" package is for multiway percentage tables. I've posted a first effort called "ctab" on this group and a request for enhancing "ftable" with percentages on the wishlist. The "mcl" package is for estimating multinomial logistic models using conditional logistic

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 R users, I have a couple of linear model related questions. 1) How do I produce a fixed effect linear model using lme? I saw somewhere (this may be Splus documentation since I use Splus and R interchangeably) that using lme(...,random= ~ -1 | groups,...) works, but it gives the same as lme(...,random= ~ 1 | groups,...), ie. fits a random effect intercept term. The reason why I want to do

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 <-