similar to: Re: variations on the theme of survSplit

Hi, I am new to R so forgive me if the following query is somewhat simple.  I have a small tab-separated file with n records of glucose values, one record per day, seven measurements per day. It looks like this: date    sober    no    vm    nm    va    na    vs 20091229    NA    6.8    NA    2.7    11.7    2.7    6.2 I'd like to make a graph on which the glucose day curves are plotted

To Danardonos concern of splitting time for records with delayed entry: This can fairly easily be accomodated, by simply splitting time in small intervals of time since entry into the study, and then compute the value of the other timescales for each of these e.g.: current.age <- time.from.entry + age.at.entry but the cut on the other timescales will not be exactly where you may want them

Full_Name: Hugh C. Pumphrey Version: 1.4.1 OS: Linux (Debian Woody) Submission from: (NULL) (129.215.133.170) The function readChar() appears to have some type of problem with memory allocation. I don't know if "memory leak" is the correct term but if one uses readChar() many times, the R binary grows in size until it eats all your memory and swap space. The code enclosed below

I have three time series, x, y, and z, and I want to analyse the relations between them. However, they have vastly different resolutions. I am writing to ask for advice on how to handle this situation in R. x is a stimulus, and y and z are responses. x is a rectangular pulse 4 sec long. Its onset and offset are known with sub-millisecond precision. The onset varies irregularly -- it doesn't

I would be grateful for advice about the following problem. It's not directly R-related, but I'm hoping that R will help me analyse the following data. I have a table which indicates the progression of a certain age-related disease. At a certain point in time, a population was sampled; and I have measurements for the age of each individual, and their disease stage. (Disease stage is an

How can I change the position of the ylab, after enlarging the margins with par(mar=...)? Here is the relevant code snippet ---- par(mar=c(5.1,5.1,4.1,2.1)) plot(c(1979,2003),c(40,50),ylim=c(1,73),lab=c(20,10,1),pch=21,col='blue',bg='blue',axes=FALSE,xlab="Years",ylab="Onset/Withdrawl Date",font.lab=2) box() axis(1,las=2)

I am writing to get a better handle on a warning I am getting from a coxph analysis I am doing. I am analysing age of onset of dementia *after* the onset of parkinson disease. My data looks like: age.park age.dem age.death censor x1 x2 x3 x4 1 76 87 88 0 16 33 E3 E3 2 75 84 84 0 33 36 E3 E3 3 77 81 81 1 NA NA

Dear All, Writing sooner than I thought I'd need to. I'm using R 2.4 on Mac OS X, with RODBC, PostgreSQL 8.1 and Actual's ODBC driver. I have all my data in Filemaker 8.5, but it is automatically exported into PostgreSQL for analysis as Filemaker's ODBC and JDBC access is awful, slow and has a tendency to crash. I have disability data where for each patient there is a survival

Dear All, since years I am struggling with Surv objects in data.frames. The following seems to have to do with it. See below the modified example from the help page of survSplit. The original works, as expected. If, however, a Surv object is added to the data.frame, each record gets doubled. Is there some solution other than avoiding Surv objects in data.frames? Thanks, Heinz

Dear R-users, I use the survsplit function in the survival package to change my data into counting-process format and the transformed format is as follow: (a) start stop event DP age .... 0 5 0 1 20 5 10 0 1 20 10 25 1 1 20 looking at the above three entries that belong to the same person, if an event happen at

Hola a todos, -Estoy estudiando el efecto de dos genotipos (~tratamientos) en la aparición de síndrome metabólico (MetS) con datos longitudinales recogidos a tiempo 0,7,10,15,20 y 25 años. -He hecho un dataframe con las siguientes variables MetS: Síndrome Metabólico (Si=1,No=0) bmi: Indice de masa corporal (IMC) cuando se produce la conversión a MetS+ . Para los que permancen MetS-, esta variable

In increasing the computation time and bit rate with VBR: Has anyone considered implementing the standard audiological recognition technique of using the duration of zero energy (voice onset timing) to identify the presence of voiceless sounds? I would like a means of determining whether or not a given window will be full of voiced speech or not. Matt -- The swallow may fly south with the sun

Hi guys, just wanted to ask, on the onset of our page, we generate several div''s. What we noticed on an intermittent connection is that several divs don''t get to generate all the div''s sometimes so we had to reload our page. Would you have any technique on how we can do this? thanks bing -- Posted via http://www.ruby-forum.com/.

hello R-world, according to the strucchange package .pdf, "all procedures in this package are concerned with testing or assessing deviations from stability in the classical linear regression model." i'd like to test/assess deviations from stability in the Poisson model. is there a way to modify the strucchange package to suit my purposes, or should i use be using another

Hi I am sure that this question has been asked before ... appologies in advance This - which comes out very nicely - better than the commercial stuff. plotmeans (cdpy~Dodefordpy, Data = Dataset, connect = False, minbar = 1, mean.labels = FALSE, col = "blue", barwidth = 1.5, barcol = "red", ci.label = FALSE, xlab="Onset", pch = 15, par(las =2)). Only one snag I

Happy New Year, r-helpers! I am using lowess to smooth a scatter plot, xx<-lowess(xinput,f=.04) #defaults for other args followed by turnpoints(xx$y) #defaults for other args I plot the smoothed result as well as turnpoints (using yy$tppos) on top of raw data plot. Result is exactly as expected, graphically. For another purpose, I calcuate the difference between turnpoints (representing

On Fri, Nov 14, 2008 at 5:11 AM, Hans J. Koch <hjk at linutronix.de> wrote: >> I don't think anything currently in Ogg can do this. The closest I can >> think of is CMML's clip concept, but you'd still have to parse the entire >> stream to find them all. > > That's nearly useless for DVD-like applications. I don't know. Wouldn't it work if

Another request for help implementing the 'apply' functions to avoid a loop structure... I am working with a data set that includes lab measurements taken at different dates for the subjects, with some subjects having more results than others. I would like to average lab results for each subject that were taken on the same day. I can do this using a for loop, but would like to know how

I am following a document teaching how to use regression and right at the onset I get an R error. I understand that variables "conc" and "signal" should have the same length but I am using the what R manual suggests to drop the error and it is not cooperating. What gives? The manual says that the default is na.ombit by the way, not that it's addition does anything How

Hi, I have some general questions about statistical analysis for a research dataset and a request for advice on using R and associated packages for a valid analysis of this data. I can only pose the problem as how to run multiple ANOVA tests on time series data, with reasonable controls of the family-wise error rate. If we run analysis at many small sections of a long time-series, the Type-I