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Hi All, I have conducted the following survival analysis which appears to be OK (thanks BRipley for solving my earlier problem). > surv.mod1 <- survreg( Surv(timep1, relall6)~randgrpc, data=Dataset, dist="weibull", scale = 1) > summary(surv.mod1) Call: survreg(formula = Surv(timep1, relall6) ~ randgrpc, data = Dataset, dist = "weibull", scale = 1)

Hi All, I am currently conducting a mixed model. I have 7 repeated measures on a simulated clinical trial. If I understand the model correctly, the outcome is the measure (as a factor) the predictors are clinical group and trial (1-7). The fixed factors are the measure and group. The random factors are the intercept and id and group. I tried using 2 functions to calculate mixed effects.

Hi All., Following on from all the help I received earlier .. Just so as you know a well known text book (T&F) uses "repeated observations" - I think perhaps they meant repeated measures in retrospect I am a total beginer to ARIMA, but have read a a basic text. I read that ARIMA requires a minimum of 50 repeated measures in time (repeated measures)- Unfortunately, I have 8

Hi., I would like to conduct a CHAID tree analysis - Chi-square Automatic Interaction Detector. >From what I can make out from MASS, tree, rpart, and a quick search it isn't available as a package. Is that correct or have I missed it? Has anyone an available implementation of it? Thanks Stephen Nana Mail <http://mail.nana.co.il> - Get Your Free Personal Outlook 2003 Now

Hi I'm conducing a meta-analysis using the meta package. Here's a bit of code that works fine - tmp <- metacont(samplesize.2, pctdropout.2, sddropout.2, samplesize.1, pctdropout.1, sddropout.1, data=Dataset, sm="WMD") I would now like to control for a couple of variables (continuous and categorical) that aren't in the equation. Is meta

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

Hi All, I am currently conducting some survival analyses. I would like to extract coefficients at each level of the IVs. I read on a previous posting that dummy regression using coxph was not possible. Therefore I though, hey why not categorize the variables (I realize some folks object to categorization but the paper I am replicating appears to have done so ...) and turn the variables

Hi I have been asked to provide Weibull parameters from a paper using Kaplan Meir survival analysis. This is something I am not familiar with. The survival analysis in R works nicely and is the same as commercial software (only the graphs are superior in R). The Weibull does not and produces an error (see below). Any ideas why this error should occur? My approach may be spurious.