similar to: Hmisc and Design Packages

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

The Hmisc and Design libraries have been updated respectively to versions 1.4-2 and 1.1-1. New versions for Linux/Unix/Windows may be obtained from http://hesweb1.med.virginia.edu/biostat/s/library/r . Web sites for the libraries are http://hesweb1.med.virginia.edu/biostat/s/Hmisc.html and http://hesweb1.med.virginia.edu/biostat/s/Design.html . Thanks to Xiao Gang Fan for porting the libraries

The Hmisc and Design libraries have been updated respectively to versions 1.4-2 and 1.1-1. New versions for Linux/Unix/Windows may be obtained from http://hesweb1.med.virginia.edu/biostat/s/library/r . Web sites for the libraries are http://hesweb1.med.virginia.edu/biostat/s/Hmisc.html and http://hesweb1.med.virginia.edu/biostat/s/Design.html . Thanks to Xiao Gang Fan for porting the libraries

The Hmisc and Design libraries have had major improvements. New versions for Linux/Unix/Windows may be obtained from http://hesweb1.med.virginia.edu/biostat/s/library/r Thanks to Xiao Gang Fan for porting the libraries to Windows once again. Change logs may be obtained from http://hesweb1.med.virginia.edu/biostat/s/library/common Several bugs have been fixed in Design, and many bugs have been

I am pleased to announce that a new version of the Hmisc package, 2.0-0, is on CRAN. Thanks to Kurt Hornik and Uwe Ligges and others for making this possible. Thanks also to Xiao Gang Fan who has generously ported Hmisc and Design to Windows multiple times until now. The Design package will soon be on CRAN too. --- Frank E Harrell Jr Prof. of Biostatistics & Statistics Div. of

I am pleased to announce that a new version of the Hmisc package, 2.0-0, is on CRAN. Thanks to Kurt Hornik and Uwe Ligges and others for making this possible. Thanks also to Xiao Gang Fan who has generously ported Hmisc and Design to Windows multiple times until now. The Design package will soon be on CRAN too. --- Frank E Harrell Jr Prof. of Biostatistics & Statistics Div. of

Dear all; I am trying to use HMISC imputation function to perform multiple imputations on my data and I keep on getting errors for the code given in the help files. When using "aregImpute" the error is; >f <- aregImpute(~y + x1 + x2 + x3, n.impute=100) Loading required package: acepack Iteration:1 Error in .Fortran("wclosepw", as.double(w), as.double(x),

I had this problem too. The documentation for the winMenus says that these functions are part of the utils package. R must be loading this library after it sources in Rprofile. If you add library(utils) to the beginning of your .First function, it should take care of the problem. Brian Gregor, P.E. Transportation Planning Analysis Unit Oregon Department of Transportation Brian.J.GREGOR at

Hi folks, Using R Version 1.6.2 (2003-01-10) on SuSE Linux 7.2, I just installed Hmisc_1.5-3.tar.gz and Design_1.1-5.tar.gz These were taken from http://hesweb1.med.virginia.edu/biostat/s/library/r Checked the dependencies: Hmisc: grid, lattice, mva, acepack -- all already installed Design: Hmisc, survival -- survival already installed, so installed Hmisc first All seems to go

Hello R experts I am using Hmisc aregImpute and Impute (following example on page 105 of The Hmisc and Design Libraries). *My end goal is to have NAs physically replaced in my dataframe. I have read the help pages and example in above sited pdf file, but to no avail. Here is example of what I did. Ph, my data frame, is attached. > xt <- aregImpute (~ q5 + q22rev02 + q28a, n.impute=10,

Hi, I have a dataframe ds1.2 - 503 categorial variables and 1 continuous response variables. I ran aregImpute to deal with NA's and got the followig error: > fmla = terms( Response ~ . ,data=ds1.2) > ds.i = aregImpute(fmla,data=ds1.2) Error in matrix(as.double(1), nrow = n, ncol = p, dimnames = list(rnam, : length of dimnames [2] not equal to array extent Could you explain

New versions of source packages for Hmisc and Design are available from CRAN for R 1.9 on Linux/Unix. Knowing Uwe Ligges, a Windows binary is not far behind. Changelogs are at http://biostat.mc.vanderbilt.edu/twiki/bin/view/Main/ChangelogHmisc and http://biostat.mc.vanderbilt.edu/twiki/bin/view/Main/ChangelogDesign . These versions pass R CMD check for the latest R-devel so I expect they

New versions of source packages for Hmisc and Design are available from CRAN for R 1.9 on Linux/Unix. Knowing Uwe Ligges, a Windows binary is not far behind. Changelogs are at http://biostat.mc.vanderbilt.edu/twiki/bin/view/Main/ChangelogHmisc and http://biostat.mc.vanderbilt.edu/twiki/bin/view/Main/ChangelogDesign . These versions pass R CMD check for the latest R-devel so I expect they

I have always avoided missing data by keeping my distance from the real world. But I have a student who is doing a study of real patients. We're trying to test regression models using multiple imputation. We did the following (roughly): f <- aregImpute(~ [list of 32 variables, separated by + signs], n.impute=20, defaultLinear=T, data=t1) # I read that 20 is better than the default of

Hello, I would like to use the aregImpute and fit.mult.impute to impute missing values for my dataset and then conduct logistic regression analyses on the data, taking into account that we imputed values. I have no problems imputing the values using aregImpute, but I am getting an error at the fit.mult.impute stage. Here is some sample code (I actually have more observations and variables to

Great news! Having spent a significant amount of time porting/mantaining some packages on S4/Splus 5.x and R, I'm interested in your experience as you port Hmisc and Design; I'd like to know of your progress in order to help improve available R/Splus portability tools both at the C and the S language code. These tools include the package SLanguage written by John Chambers, and

After several nice and informative discussions with Bill Venables, Duncan Temple Lang, Tony Rossini, Martin Maechler, and Robert Gentleman, I am pleased to announce that I am becoming an R user and have begun porting the Hmisc and Design libraries to R. I have been very impressed with the maturity of R and with its logical design, particularly its online documentation setup and the way packages

After several nice and informative discussions with Bill Venables, Duncan Temple Lang, Tony Rossini, Martin Maechler, and Robert Gentleman, I am pleased to announce that I am becoming an R user and have begun porting the Hmisc and Design libraries to R. I have been very impressed with the maturity of R and with its logical design, particularly its online documentation setup and the way packages

Hello, I'm working on a Cox Proportional Hazards model for a cancer data set that has missing values for the categorical variable "Grade" in less than 10% of the observations. I'm not a statistician, but based on my readings of Frank Harrell's book it seems to be a candidate for using multiple imputation technique(s). I understand the concepts behind imputation, but using