similar to: Mean survival times

Hello, My advisor and I have been working on some survivorship analyses in R and we are hoping to get some feedback on a particular issue involving nesting. We are interested in patterns of food discovery by ant species. Our observations consist of time to discovery by an ant for three different food types, each of two different sizes. These data were collected at 6 plots located in each of

Dear R users, I have a question regarding the use of xyplot in the lattice() package. I have two factors (each with two levels), and I??d like to change the order of the panels in a 2x2 panel layout from the default alphabetic order that R uses based on the names of the factor levels. My approach is (in principle) xyplot(y~x|Factor1+Factor2) Let??s assume, my factor levels for Factor1 are A

All, I had a question about the JGR console and whether or not I can manipulate the location where line wrapping occurs. I have searched 'JGR' in the R listserve archives and attempted to find console manipulation on the JGR website to no avail and could use some direction. TIA, Bret As an example, the below output wraps every 4th value, leaving about 2/3 of the console empty. >

Dieter (et. al.) I am weak and therefore yield to temptation... This is OT for R, so stop reading and discard now if you're looking for real R Help. (see also one inline coment below) Mount soapbox; begin rant </ In addition to the points you made/alluded to, may I also suggest that confusion about the nature of The Experimental Unit results in a lot of garbage/non-replicable scientific

This was accidentally addressed to me, personally: ------- start of forwarded message ------- Message-id: <39F70D7C.287323D5 at yale.edu> MIME-version: 1.0 From: Diana Fischer <diana.fischer at yale.edu> To: maechler at stat.math.ethz.ch Subject: R-Project Date: Wed, 25 Oct 2000 11:42:36 -0500 I briefly looked at the R documentation of available statistical procedures. It appears

Dear List, Is there an automated way to use the survival package to generate survival rate estimates and their standard errors? To be clear, *not *the survivorship estimates (which are cumulative), but the survival *rate * estimates... Thank you in advance for any help. Best, Brian [[alternative HTML version deleted]]

Hi, I am attempting to plot survival curves estimated by cox proportional hazards regression model. The formula for the model is this: F.cox.weight <- coxph(Surv(Lifespan, Status) ~ MS + Weight + Laid + MS:Laid + Weight:Laid, data = LongF) MS = Mating status (mated/virgin) Weight = adult female weight, continuous covariate Laid = number of eggs laid by each female, continuous covariate I

Hi All, I am trying to do a survivorship analysis with library(survival)from a data set that looks like this: I followed a bunch of naturally germinated seedlings of an annual plant from germination to death (none made it to reproduce, and died in a period of ~60 days after germination.) I also know the size of the seed of every individual censused. So I am trying to analyze seedling survival as

Dear All, I would like to ask a couple of questions on a LME model. I tested 4 selection lines at 4 food concentrations against a standard competitor stock. I had 3 replicate cages per selection line. In each cage I have 10 vials. I counted the number of wild type flies and competitor stock emerging in each vial. My main question is: is there any difference between selection lines? I did fit

All, I am struggling to create a survival plot using LTRC data for each year of a 10 year period. I have a set of individuals (birds) where 'entry' is the day of the year (1-365) they are released (let out of pens) into the wild (2 year data snip below). 'Entry' (e.g., day of year the first bird is released for each year) is highly variable, ranging from 48 to >250. When I

Hi. I used coxph(surv(start,end,event)~~event,data) to deal with interval censor data. Does anyone know similar samples using coxph(surv(start,end,event)~~event,data)? If you knows, can you tell me? I'll really appreciate it. Thank you very much R learner.

I had my eye on the Tyan dual-Opteron mobos for awhile. I tried to find a posting *anywhere* sharing experiences with these boards under Linux. No such luck. So placing myself under the heading "Where Angles Fear to Tread," I went ahead and built a system anyway. Here's what I've learned. The specs: Tyan Thunder K8SE S2892, BIOS 1.01 2x Opteron 270, 2Ghz Dual-Core, retail

Hi, I am performing a repeated measures 2-way ANOVA to assess the influence of plant and leaf on aphid fecundity. Fecundity is measured for each aphid on a single leaf. Here is what I typed. wingless <- reshape(Wingless, varying =

Hello all. This may be a trivially simple question to answer, but I'm a little bit stumped with respect to the calculation of the F statistics in nested anovas in R. If I understand correctly, the F statistic for the among-subgroups but within groups hypothesis is calculated as MS_subgroups/MS_error, while the F statistic for the factor is calculated as MS_factor/MS_subgroups (I'm

Dear list members, In re-running with GLMM() from the lme4 package a generalized-linear mixed model that I had previously fit with glmmPQL() from MASS, I'm getting a warning of a convergence failure, even when I set the method argument of GLMM() to "PQL": > bang.mod.1 <- glmmPQL(contraception ~ as.factor(children) + cage + urban, + random=~as.factor(children) + cage +

Hello, I am trying to compute the mdeian of the survival time from the function survfit: > fit <- survfit(Surv(time, status) ~ 1) > fit Call: survfit(formula = Surv(time, status) ~ 1) records n.max n.start events median 0.95LCL 0.95UCL 111 111 111 20 NA NA NA The results is NA? the fit$surv gives values between 1 and 0.749! Am I doing this correct?

Lately R has been behaving strange on my Linux (Ubuntu 7.10) machine, with occasional segfaults. Today something else and reproducible happened: If I type the code below (meant for calibrating data), I get the error message that "the C stack usage is too close to the limit". calcurve <- cbind(1:2e4, 1:2e4, 1:2e3); #dummy curve, real one is more complex caldist <-

Dear all, I wrote the following batch script on a iMac, and ran it on a linux mosix cluster. tu <- read.table("cage.mm5.tags.rna_lib.CAA-CAJ.tu-reshape.table") tu_reshaped <- t(reshape(tu[1:50,], direction="wide", timevar="tu", idvar=c("rna","lib"))) write.table(tu_reshaped, "cage.mm5.tags.rna_lib.CAA-CAJ.tu-reshaped.table")

Not sure if anyone has seen this probably not but thought that I would throw it out there, running centos 5.4 on a tyan s4985 and I wanted to add a hot swap drive cage to it so I added a supermicro cage and bought some drives, not wanting raid but something for storage and rsync backups. Well I decided to buy some of the newer samsung F3 hd502hj 500Gig hard drives, supposedly good performance for

Dear All, I would like to ask you how to accomplish a little tricky data manipulation. I have a large dataset, looking something like: temp line cage number 18 18 1 6678.63 18 18 1 7774.458 18 18 1 7845.902 18 18 1 9483.578 18 18 1 8983.555 18 18 1 9181.052 18 18 1 9458.696 18 18 1 8138.616 18 18 1 7981.994 18 18 1 7556.491 18 18 1 7672.137 18 18 1 6607.776 18 18 1 8383.65 18 18 1 7129.852 18 18