similar to: missing data dataset needed

Hi. I've a dataframe with about 46000 women with about 500 cases (cancers). I want to define a case-cohort sampling scheme, matching by age and hospital centre. Is there anyone who has already written a code for that? It should be something similar to the stcacoh macro in Stata. TIA, Stefano -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list

Hi all, I'm currently working with a dataset that has quite a few missing values and after some investigation I figured that multiple imputation is probably the best solution to handle the missing data in my case. I found several references to functions in S-Plus that perform multiple imputation (NORM, CAT, MIX, PAN). Does R have corresponding functions? I searched the archives but was not

The Design library has been fully ported to R except for Cox proportional hazards regression modeling (using Therneau's survival package) which will be available in about two weeks. It will take much longer to make all the example code executable, is it currently contains many examples for which data are not provided. Thanks to Xiao Gang Fan <xiao.gang.fan1 at libertysurf.fr> who

The Design library has been fully ported to R except for Cox proportional hazards regression modeling (using Therneau's survival package) which will be available in about two weeks. It will take much longer to make all the example code executable, is it currently contains many examples for which data are not provided. Thanks to Xiao Gang Fan <xiao.gang.fan1 at libertysurf.fr> who

Hello I am sure this is elementary, but I can't figure it out..... I am using SAS v8.2 and R 1.4.1 on a Windows platform. I have a large (6 megabyte) file in .sd2 format. I want to import it into R. If necessary, I can create a smaller file, as I don't need all the variables that are in this file I downloaded the package foreign. I then tried to use PROC CPORT in SAS to create a

Dear lister Few months ago I posted a question about the drawing of a nomogram through R routines. Any news? thanks _________________________________________________________________ http://join.msn.com/?page=features/junkmail -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send

Hello R-user I want to compute a multiple regression but I would to include a check for collinearity of the variables. Therefore I would like to use a ridge regression. I tried lm.ridge() but I don't know yet how to get p-values (single Pr() and p of the whole model) out of this model. Can anybody tell me how to get a similar output like the summary(lm(...)) output? Or if there is

In a .First.lib I want to issue two require()s to insure that two other packages are loaded. But I want the package being loaded by .First.lib using library.dynam("mypackage",pkb,lib) to be higher in the search order than the two required packages, because I want to have a couple of functions from the two required packages overridden. What is the best way to do that? Thanks in advance

I am unclear on whether to run R CMD check pgkname as user or as root on Linux. When running as user, after all the latex, html, and man files are created, I get the following error message: Rdconv(): Couldn't open '': Permission denied Has anyone dealt with that message? When I run R CMD check a second time, all latex, html, etc. are recreated which takes quite a while. Is there

I am using GLM to calculate logit models based on cross-sectional data. I am now down to the hard work of making the results intelligible to very average readers. Is there any way to calculate a psuedo analoque to the R^2 in standard linear regression for use as a purely descriptive statistic of goodness of fit? Most of the readers of my report will be vaguely familiar and more comfortable with

Dear all, I'm trying to solve the following problem, and hoping to get some advise here from the group I have a dataframe in which the same sample was measured more than once on the same day. I would reorganize the dataframe to get a single value (mean /median) for one day Patient Day Sample Test A 1 A 23 A 1 A 36 A 5 B 44 A 5 B 23 B 2 C 10 B 2 C 5 mean Patient Day Sample Test.mean A

I'm interested in finding a package in R to do multiple imputations, similar to MICE in S-Plus. Does one exist? Please email directly to me. Thanks. Linda Andrews Social & Scientific Systems (301) 628-3234 -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info",

i make me thoughts about a "advanced tabulation" package similar to commercial software products like Quantum or Wincross. Before i'm beginning to fight with coding - is in the mailing-List anybody doing something similar in the past and have a good starting point and/or suggestions for me ? My purpose ist to define for a dataset headers (i.e. sex,age-groupes..) which should

Dear list members, I have a statistical question, that doesn't belong to this list, and I apologise for that in advance but I would appreciate your help very much. Is there some convention for selecting the a level for significance testing in scientific (e.g. chemical processes) studies? Most people use the 0.05 level but I could not find a reference to justify this. Why not 0.01 or 0.1?

Hello, Does R have any capabilities (or are there any add on packages) which can do estimation of standard statistical models (means, regression, logistic regression, etc) which take into account not only weights (e.g. post-stratification weights) but also the sample design, such as stratification and clustering information (to compute a robust taylor linearized variance estimator, for

Hi, I've read in the past a thead in the R discussion list about the multi-valued type variable (what was called checklist). At the moment Gregory had intention to add some general code in his gregmisc package. I'm wondering if there's some general code / packages available ? A general class for taking account this type of variable would be very useful in the domain of survey

Hi, For computing correlation among variables in a matrix, I use cor( ), but for computing the p-values I'm using cor.test in the following way: cor.p <- function(X) { res <- matrix(0, ncol(X), ncol(X)) for (i in 1:ncol(X)) for (j in 1:ncol(X)) res[i, j]<- cor.test(X[, i], X[, j])$p.value rownames(res) <- colnames(res) <- colnames(X) res } I'm just wondering if there is a

My wife has been using a diagnostic from Manley (1991; "Randomization and MonteCarlo Methods in Biology") that compares a normal multiple regression's performance with that using random predicted variables. Is there something like this already available in R? If not, the "boot" package looks like a good place to start looking for methods, no? Thanks in advance Bruce

Hello R users, I am looking for R (or S) code related to group sequential or adaptive designs for clinical trials. (The most prominent examples are the designs of Pocock or O'Brien/Fleming, the alpha-spending function approach, or Fisher's combination test and the inverse normal method.) I am particularly interested in the calculation of the critical boundaries, the handling of spending

Hello, I have developed a R package. When I pass it through the rcmd check command (windows 98) to see if it is ok, everything works fine until there is a call to the program "sh.exe". It seems that rcmd calls it with the option --c, but it doesn't seem to be a legal option. After that, when rcmd tries to create a directory inside the "package_name.Rcheck" directory there