similar to: baseline on snv_130 for SUNWstc-fs-zfs?

Hello, I was doing performance testing, validating zvol performance in particularly, and found that zvol write performance to be slow ~35-44MB/s at 1MB blocksize writes. I then tested the underlying zfs file system with the same test and got 121MB/s. Is there any way to fix this? I really would like to have compatible performance between the zfs filesystem and the zfs zvols. # first test is a

Hi, assume that I have a repeated measure dataset with 3 time points: baseline, day 5 and day 10. There are 4 treatment groups (vehicle, treatment 1, treatment 2 and treatment 3). 20 subjects per treatment group. A simple straight-forward way to analyze the data is to use mixed model: model 1: obj <- lmer(y ~ treatment * time +(time|subject)) where time is numeric with value 0,5 and 10.

Hello all, I'm almost embarrassed to post this , it seems so easy. Suppose I have a baseline and follow up survey but some people are missing in the follow up: > baseline<-data.frame(id=c(3,5,7,9,12), data= runif(5)) > follow.up<-data.frame(id=c(3,7,9,12), data= runif(4)) > baseline id data 1 3 0.66771988 2 5 0.28794744 3 7 0.01892821 4 9 0.64863175 5 12 0.86485882

Dear R-help and Prof. Harrell: My question concerns the baseline state for continuous variable in lrm() within the RMS package. I have a model which can be reduced to: lrm(FT ~ rcs(V1, c(0, 1,5)) The model makes perfect sense if the baseline state is where V1>=5 but the model makes no sense if the baseline category is 0 (which I had expected). Can someone point me to a reference, or

Hi all, I asked this before the holiday, didn't get any response. So would like to resend the message, hope to get any fresh attention. Since this is not purely lme technical question, so I also cc-ed R general mailing list, hope to get some suggestions from there as well. I asked some questions on how to analyze longitudinal study with only 2 time points (baseline and a follow-up)

Dear R-help, I am trying to obtain the baseline survival estimate of a fitted Cox model (S_0 (t)). I know that previous posts have said use 'basehaz' but this gives the baseline hazard function and not the baseline survival estimate. Is there a way to obtain the baseline survival estimate or do I have to use the formula which does something like S(t) = exp[- the integral from 0 to t of

-------- Original Message -------- Subject: Re: How to obtain nonparametric baseline hazard estimates in the gamma frailty model? Date: Mon, 04 Nov 2013 17:27:04 -0600 From: Terry Therneau <therneau.terry at mayo.edu> To: Y <yuhanusa at gmail.com> The cumulative hazard is just -log(sfit$surv). The hazard is essentially a density estimate, and that is much harder. You'll notice

Dear All, I'm having just a little terminology problem, relating the language used in the Hosmer and Lemeshow text on Applied Survival Analysis to that of the help that comes with the survival package. I am trying to back out the values for the baseline hazard, h_o(t_i), for each event time or observation time. Now survfit(fit)$surv gives me the value of the survival function, S(t_i|X_i,B),

Dear R-help mailing list,   Kindly help me out with this problem:   I have a dataset that is in the format below, ID  time  Y  Age 1  0     195  23.1 1  2    204  23.3 1  4   202    23.5 2  0  170    22.0 2  3   234   22.2 3  0  208   24.4 3  2  194  24 .7 3  3   204  24.9   I wish to remove all the measurements at time point 0 and convert them to a baseline variable as follows;    ID  time  Y 

? Hallo, which function can i use to do (baseline) logistic regression + goodness of fit tests? so far i found: # logistic on binary data lrm combined with resid(model,'gof') # logistic on binary data glm with no gof-test # baseline logit on binary data

I am pretty pleased with baselines I fit to chromatograms using the runquantile() function in caTools(v1.6) when its probs parameter is set to 0.2 and its k parameter to ~1/20th of n (e.g., k ~ 225 for n ~ 4500, where n is time series length). This ignores occasional low- side outliers, and, after baseline subtraction, I can re-adjust any negative values to zero. But runquantile's

Dear all, I am confused with the output of survfit.coxph. Someone said that the survival given by summary(survfit.coxph) is the baseline survival S_0, but some said that is the survival S=S_0^exp{beta*x}. Which one is correct? By the way, if I use "newdata=" in the survfit, does that mean the survival is estimated by the value of covariates in the new data frame? Thank you very much!

Hello everybody. First of all, I would like to present myself. I'm a french student in public health and I like statistics though I'm not that good in mathematics (but I try to catch up). I've discovered R recently while trying to find a statistical program in order to avoid rebooting my computer under windows when I need to do some statistical work. And here is my first question.

"Old" tested version had not actually been tested; therefore in this flight we test it, rather than a new candidate. The baseline, if any, is the most recent actually tested revision. flight 20079 qemu-upstream-4.3-testing real [real] http://www.chiark.greenend.org.uk/~xensrcts/logs/20079/ Failures :-/ but no regressions. Tests which did not succeed, but are not blocking:

"Old" tested version had not actually been tested; therefore in this flight we test it, rather than a new candidate. The baseline, if any, is the most recent actually tested revision. flight 20078 qemu-upstream-4.2-testing real [real] http://www.chiark.greenend.org.uk/~xensrcts/logs/20078/ Failures :-/ but no regressions. Tests which did not succeed, but are not blocking:

Hi, If I fit a Cox model using "coxph", is there a R function so that I could obtain the estimate of baseline survival function? Thank you. Zheng -- Zheng Yuan Ph.D student Department of Biostatistics University of Michigan Ann Arbor, MI 48109

Hi all, I'm trying to fit a Cox regression model with two factor variables but have some problems with the interpretation of the results. Considering the following model, where var1 and var2 can assume value 0 and 1: coxph(Surv(time, cens) ~ factor(var1) * factor(var2), data=temp) What is the baseline model? Is that considering the whole population or the case when both var1 and var2 =

Greetings -- I would like to estimate a proportional hazard model with a weibull or lognormal baseline. I have looked at both the coxph() and survreg() functions and neither appear (to me ) to do it. Am I missing something in the docs or is there another terrific package out there that will do this. Many Thanks. Carl Mason

Dear R-users, I would like to fit a fully parametric proportional hazard model with a weibull baseline hazard and a logit link function. This is, the hazard function is: lambda_i (t) = lambda_0 (t) psi (x_i* beta) where lambda_0 is a weibull distribution and psi a logistic distribution. Does someone know a package and/or function on R to do this? Thanks. -- M.L. Avendaño [[alternative HTML

Hello everyone, I want to fit a baseline category logit model (with 3-4 categories) with nested random effects. (For example, I have clusters(i) and households within clusters (j) resulting in the nested random effects structure : b_i +d_j(i)). Is there a R function/package that I can use ? Any help will be much appreciated. Thanks and regards, Dhiman Bhadra [[alternative HTML version