similar to: Second-order effect in Parametric Survival Analysis

To R programming experts, I am a undergraduate student, and now doing research personally. I apply diagonal bivariate poisson (R package "bivpois") with stochatics weighted function (refer to dixoncoles97 section 4.5 to 4.7). However I dont know how to fit this stochatical weighted function to the completed bivariate poisson model. I know that some other references for dynamic soccer

Hi All, First method:- >library(XML) >theurl <- "http://home.sina.com" >download.file(theurl, "tmp.html") >txt <- readLines("tmp.html") >txt <- htmlTreeParse(txt, error=function(...){}, useInternalNodes = TRUE) >g <- xpathSApply(txt, "//p", function(x) xmlValue(x)) >head(grep(" ", g, value=T)) [1] " |

Hi R users I need to estimate, with kaplan Meier methodology, a Quality adjusted survival analysis. It is possible doing this at R? Thanks in advance. Best Regards Pedro Mota Veiga -- View this message in context: http://r.789695.n4.nabble.com/How-can-I-doing-Quality-adjusted-survival-analysis-in-R-tp4295868p4295868.html Sent from the R help mailing list archive at Nabble.com.

David's answers were correct. You are looking deep into the code when there is no reason to to so. 1. h(t|(X=x,Z=z)) = exp(Beta0 + XZBeta1) Most statisticians will tell you that this is an unwise model. The reason is that if you replace X with "X+1" the fit changes, which is almost never desirable. What if someone coded your dummy variable as 1/0 instead of 0/1 -- wouldn't

I am facing the same problem as well, I would like to code with following concept but wondering how to cope it. if (*mdb file exist) { add new row/col } else { add new *mdb file } ---------------------------------------------------------------------------------------------- From: Uri Shimron <UriShimron_at_optiver.com> Date: Thu 21 Sep 2006 - 13:27:35 GMT First of all,

Assume that we collect below data : - subjects = 20 males + 20 females, every single individual is independence, and difference events = 1, 2, 3... n covariates = 4 blood types A, B, AB, O http://r.789695.n4.nabble.com/file/n4245397/CodeCogsEqn.jpeg ?m = hazards rates for male ?n = hazards rates for female Wm = Wn x ?, frailty for males, where ? is the edge ratio of male compare to female Wn =

Dear list members, Is there a package somewhere for jointly estimating two poisson processes? I think the closest I've come is using the "SUR" option in the Zelig package (see below), but when I try the "poisson" option instead of the "SUR" optioin I get an error (error given below, and indeed, reading the documentation of the Zelig package, I get the impression

I've had the chance to use a testsystem here and couldn't resist running a few benchmark programs on them: bonnie++, tiobench, dbench and a few generic ones (cp/rm/tar/etc...) on ext{234}, btrfs, jfs, ufs, xfs, zfs. All with standard mkfs/mount options and +noatime for all of them. Here are the results, no graphs - sorry: http://nerdbynature.de/benchmarks/v40z/2009-12-22/ Reiserfs

I've had the chance to use a testsystem here and couldn't resist running a few benchmark programs on them: bonnie++, tiobench, dbench and a few generic ones (cp/rm/tar/etc...) on ext{234}, btrfs, jfs, ufs, xfs, zfs. All with standard mkfs/mount options and +noatime for all of them. Here are the results, no graphs - sorry: http://nerdbynature.de/benchmarks/v40z/2009-12-22/ Reiserfs

ryusuke: It sounds like you need to have (D)COM server to be able to work on the background. As for the foreground, rcom is what you need and it appears to be working OK. I am out of my office right now but I'll back to work next week and I will be able to explain in more detail how the applications I tied together work with RExcel and R. You can download the latest staconnDCOM.latest.exe from

Hi All, con <- member <- class <- list() for(i in 1:5) { con[[i]] <- odbcConnectAccess(paste("C:/Desktop/Data/source",i,".mdb",sep = '')) #read data from Access files member[[i]] <- sqlFetch(con[[i]],'member') #get table data from *.mdb files observe[[i]] <- sqlFetch(con[[i]],'observe') #get table data from *.mdb

Linus, et al, Here are the outstanding ocfs2 fixes we have for 2.6.30. The important ones are journal credit fixes for the new indexed directories. There's also MAINTAINERS update for my ocfs2 git tree. Please pull. Joel The following changes since commit 612392307cb09e49051225092cbbd7049bd8db93: Ryusuke Konishi (1): nilfs2: support nanosecond timestamp are available in the

Dear all, Is there a way to plot / calculate pointwise confidence bands or interval values for a non parametric regression like sm.regression? Thank you in advance. Regards, Martin

My data looks like this: (treatments) T1 T2 T3 DK 8 5 3 JP 5 4 1 AS 9 7 4 MK 8 4 4 DK, JP, AS, and MK are 4 different people (blocks) I am using. This is my code

I have an urgent need for assistance with a complex predictive modelling project - preliminary discussion with numerous statisticians suggests that off-the-shelf packages (including existing R libraries) are unlikely to provide a complete solution to the particular problem that we're tackling. I'm looking for a statistics consultant to help with a short (less than 4 weeks) project. The

Hi, I'm having trouble understanding how to construct a random number generator for a parametric bootstrap. My aim is to bootstrap a Likelihood Ratio statistic (under the null) for a linear model. The function at this point is given by boot.test.n01 <- function(data, indeces, maxit=20) { y1 <- fit1+se(e2)*rnorm(314) mod1 <- glm(y1 ~ X1-1, maxit=maxit) y2 <-

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Dear list, I?m wondering if there is something analogous to the TukeyHSD function that could be used for parametric terms in a GAM. I?m using the mgcv package to fit models that have some continuous predictors (modeled as smooth terms) and a single categorical predictor. I would like to do post hoc test on the categorical predictor in the models where it is significant. Any suggestions?

Dear list I am comparing the results of several different experimental setups. With kruskal.test() I can test if there is any difference at all in any of them, if I understand it correctly. But now, when there is a difference, how do I generate a (half-) table of pairwise comparisons, using e.g. wilcox.test(), to find the ones where the difference actually occurs. I guess I don't have to

We have a project on which we need to compare various methods for computing factor scores. Are there any R routines available that do parametric bootstrap or compute factor scores?