search for: suriv

...to both treatments, and i gave them 60 minutes to perform a certain behaviour. 3 of my subjects performed the behaviour in one treatment but not the other. Therefore, i need to do a survival analysis using paired data. Im little confused about how to go about this in R. Im able to perfrom a normal surival analyses not taking the paired data into account, but im just wondering if there is some way to take the pairing into account. I know there are 3 different ways to deal with grouping in the survival package, strata, cluster and frailty but i struggle to understand the meaning of these arguments a...

...> What do you think? I do not mind conveying a context to the callback. I would still prefer to keep the original min_order to check semantic though. Why? Well, it doesn't make much sense to scan low order free blocks all the time because they are simply too volatile. Larger blocks tend to surivive for longer. So I assume you would only care about larger free blocks. This will also make the call cheaper. -- Michal Hocko SUSE Labs

...> What do you think? I do not mind conveying a context to the callback. I would still prefer to keep the original min_order to check semantic though. Why? Well, it doesn't make much sense to scan low order free blocks all the time because they are simply too volatile. Larger blocks tend to surivive for longer. So I assume you would only care about larger free blocks. This will also make the call cheaper. -- Michal Hocko SUSE Labs

Hi, I'm new to R and using it for Cox survival analysis. Thanks to this great forum I learned how to compute the HR with its confidence interval. My question would be: Is there any way to get the p-value for a hazard ratio in addition to the confidence interval? Thanks, Thierry -- Thierry Panje Visiting Student Researcher Department of Psychology Stanford

Dear friends Hello I'm a PhD student in Edinburgh University and interested in survival analysis. By chance, I found design library in Splus software. I have some questions about it and it's highly appreciated if you can help me. In its help (Design library help) I found the topics(not the commands), which are available in the main software. for instance, survival analysis is available

Hello, Why the correlation between the random effects is negative? library(coxme) rats1 <- coxme(Surv(time, status) ~ (1|litter), rats) random.effects(rats1)[[1]] #one value for each of the 50 litters print(rats1) rats2 <- lmekin(time ~ (1|litter), rats) fixed.effects(rats2) random.effects(rats2)[[1]] #one value for each of the 50 litters print(rats2)

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

On Wed 26-07-17 10:22:23, Wei Wang wrote: > On 07/25/2017 10:53 PM, Michal Hocko wrote: > >On Tue 25-07-17 14:47:16, Wang, Wei W wrote: > >>On Tuesday, July 25, 2017 8:42 PM, hal Hocko wrote: > >>>On Tue 25-07-17 19:56:24, Wei Wang wrote: > >>>>On 07/25/2017 07:25 PM, Michal Hocko wrote: > >>>>>On Tue 25-07-17 17:32:00, Wei Wang wrote:

On Wed 26-07-17 10:22:23, Wei Wang wrote: > On 07/25/2017 10:53 PM, Michal Hocko wrote: > >On Tue 25-07-17 14:47:16, Wang, Wei W wrote: > >>On Tuesday, July 25, 2017 8:42 PM, hal Hocko wrote: > >>>On Tue 25-07-17 19:56:24, Wei Wang wrote: > >>>>On 07/25/2017 07:25 PM, Michal Hocko wrote: > >>>>>On Tue 25-07-17 17:32:00, Wei Wang wrote:

...onal=c(arma3.fit$arma[3],arma3.fit$arma[4],arma3.fit$arma[7]) arma4.fit$seasonal=c(arma4.fit$arma[3],arma4.fit$arma[4],arma4.fit$arma[7]) arma5.fit$seasonal=c(arma5.fit$arma[3],arma5.fit$arma[4],arma5.fit$arma[7]) #these Two are used for linearModel2 and linearModel4, Get only the #regressors that surived step removal. newcReg=cReg[match(names(linearModel2$coeff[-1]),names(cReg))] newfReg=fReg[match(names(linearModel2$coeff[-1]),names(fReg))] newmcReg=mcReg[match(names(linearModel2$coeff[-1]),names(mcReg))] newmfReg=mfReg[match(names(linearModel2$coeff[-1]),names(mfReg))] #Scenario 1 - All Regres...