similar to: k-folds cross validation with conditional logistic

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-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

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'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,

Hey, I need some help. I want to obtain a cross validation for a regression model (binary response) but I got an error with CVbinary. Well I did this: fit <- lm(resp ~ PC1 + PC2 + PC3 + PC4 + PC5 + PC6 + PC7 + PC8 + PC9+PC10+PC11+PC12+PC13+PC14+PC15+PC16+PC17+PC18+PC19+PC20+PC21+PC22+PC23+PC24+PC25+PC26+PC27+PC28, data = dexp.cp, family=binomial()) CVbinary(fit) Error in sample(nfolds, m,

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

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

Dear R users, I have a large individual-level dataset (~700,000 records) which I am performing a conditional logistic regression on. Key variables include the dichotomous outcome, dichotomous exposure, and the stratum to which each person belongs. Using this individual-level dataset I can successfully use clogit to create the model I want. However reading this large .csv file into R and running

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,

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

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 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

I am getting an error that I don't understand, and wonder if anyone could explain what's going on. I call a function defined thus: clogit.rds<-function(formula,data,extra.data,response.prob, na.action=getOption("na.action"),subset=NULL, control=coxph.control()){ method="exact" # only option for now mf<-match.call()

Hi all: My data has 3 variables: age(3levels : <30y=1 30-50y=2, >50y=3) gender(Male=0, Female=1) CD4 cell count(raw lab measurement) y(1:death 0:alive) I perform logistic regression to find out the factors that influence y. result<-glm(y ~ factor(age) + factor(gender) + CD4,family = binomial) >From the result,I can get OR(Odds Ratio) of gender via exp(Estimate of Female,

 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, 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

Hello, I'm brand new to using R. (I've been using Rapid Miner, but would like to move over to R since it gives me much more functionality.) I'm trying to learn how to do a conditional logit model. My data has one dependent variable, 2 independent variables and a "group" variable. example: class v1 v2 group "sick" .3 .7 1 "well"

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.

A multinomial logit model can be specified as a conditional logit model after restructuring the data. Doing so gives flexibility in imposing restrictions on the dependent variable. One application is to specify a loglinear model for square tables, e.g. quasi-symmetry or quasi-independence, as a multinomial logit model with covariates. Further details on this technique and examples with several