search for: baselining

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 all, I have trouble with R-beta sweaving files that include definitions with \SweaveInput in combination with keep.source = TRUE Symptom: SInput is taken from too far down the input file (the shift is the number of lines of the included file). Is that known? Searching didn't turn up anything, yet I think there are more people than just me using keep.source. Example: $

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 list, I'm trying to fit a multilevel (mixed-effects) model using the lme function (package nlme) in R 2.4.0. As a mixed-effects newbie I'm neither sure about the modeling nor the correct R syntax. My data is structured as follows: For each subject, a quantity Y is measured at a number (>= 2) of time points. Moreover, at time point 0 ("baseline"), a quantity X is

A subset of my raw data looks like this: -------------- "Grip" "Technique" "Baseline.integrated" "Task" "Stroke..direction." "Engag" "Disen" "PenDG" "PenUG" "PenDS" "PenUS" "Duration" -------------

Hi, I have encountered an issue about finding a date closest to another date So this is how the data frame looks like: PT_ID IDX_DT OBS_DATE DAYS_DIFF OBS_VALUE CATEGORY 13 4549 2002-08-21 2002-08-20 -1 183 2 14 4549 2002-08-21 2002-11-14 85 91 1 15 4549 2002-08-21 2003-02-18 181 89 1 16 4549 2002-08-21 2003-05-15

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 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

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 

Based on reading 'rgb' documentation, I would have thought the following would have produced identical results. Can someone explain how to make this happen? I need to be able to specify an array of rgb values for the 'col' parameter. colnames.col <- c("black", "red", "blue", "green") colnames.rgb <- apply(as.matrix(colnames.col), 1,

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

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.

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.

OK... I have relaid out my puppet dir as recommended in best practices: [jleggett@binford puppet]$ find . -print | grep -v .svn . ./plugins ./plugins/lines.pp ./files ./files/module ./files/module/SSH ./files/module/SSH/ssh_config ./files/module/SSH/sshd_config ./files/module/INFO ./files/module/INFO/motd ./files/module/INFO/issue ./files/module/NIS ./files/module/NIS/nsswitch.conf

Hello, I am looking for a sample size function for samples sizes, to test proportions that are not binomial proportions. The proportions represent a ratio of (final measure) / (baseline measure) on the same experimental unit. Searches using RSeek and such bring multiple hits for binomial proportions, but that doesn't seem to fit my situation. Perhaps there's some standard terminology

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!

All, How does one extract the level-1 variance from a model fit via lmer()? In the code below the level-2 variance component may be obtained via subscripting, but what about the level-1 variance, viz., the 3.215072 term? (actually this term squared) Didn't see anything in the archives on this. Cheers, David > fm <- lmer( dv ~ time.num*drug + (1 | Patient.new), data=dat.new )

Dear, I want to fit an inverse gaussion distribution to a data set. The predictor variables are gender, area and agecategory. For each of these variables I've defined a baseline e.g. #agecat: baseline is 3 data<-transform(data, agecat=C(factor(agecat,ordered=TRUE), contr.treatment(n=6,base=3))) The variable 'area' goes from A to F (6 areas: A,B,C,D,E,F) How can i

I request additional review of diff: http://reviews.llvm.org/D18762 We noticed that a source of performance difference between llvm and gcc was in dead code elimination. This diff replaces ADCE with a new implementation which will remove control flow and, under option, also remove may-be-infinite loops which are dead. The patch current has "ADCE_new.h" and "ADCE_new.cpp" to