search for: aregimpute

>From Internet I downloaded the file Hmisc.zip and used it for R package updation. and R gave the message 'Hmisc' successfull unpacked. But when I use the functions like aregImpute the package is displaying coundn't find the function Where as in help.search it is giving that use of the function > install.packages(choose.files('',filters=Filters[c('zip','All'),]), .libPaths()[1], CRAN = NULL) package 'Hmisc' successfully unpacked and M...

Hello R-help, Thanks for everyone's very helpful suggestions so far. I am now trying to use aregImpute for my missing data imputation. Here are the code and error messages. Any suggestions would be very much appreciated. Sincerely, Anders Corr ######################################## #Question for R-Help on aregImpute ######################################## #DOWNLOAD DATA (61Kb) download.fi...

From: <david_foreman at doctors.org.uk> Subject: [R] Re: extracting datasets from aregImpute objects To: <r-help at stat.math.ethz.ch> Message-ID: <1092391719_117440 at drn10msi01> Content-Type: text/plain; charset="us-ascii" I've tried doing this by specifying x=TRUE, which provides me with a single imputation, that has been useful. However, the help file pos...

hi, i'm trying to learn how to use aregImpute by doing the examples provided with the package, and after installing Hmisc.1.6-1.zip (for Windows), and running the very first example on R 1.7.1, i get an error message warning me about "mace" (see below) and acepack. i found the acepack package, but its filename ends in tar.gz and...

Hello - When trying to use Hmisc library's aregImpute function on R 1.7.0, I got the following error -- shown here using the example code from the help page --- under both Linux and Mac OS X 10.2.5: set.seed(3) x1 <- factor(sample(c('a','b','c'),1000,T)) x2 <- (x1=='b') + 3*(x1=='c') + rnorm(1000,0,2) x...

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...

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 what I shou...

Dear r-help list, I'm trying to use multiple imputation for my MSc thesis. Having good exemples using the Hmisc package, I tried the aregImpute function. But with my own dataset, I have the following error : Erreur dans matxv(X, xcof) : columns in a (51) must be <= length of b (50) De plus : Warning message: In f$xcoef[, 1] * f$xcenter :   la taille d'un objet plus long n'est pas multiple de la taille d'un objet plus court...

I've tried doing this by specifying x=TRUE, which provides me with a single imputation, that has been useful. However, the help file possibly suggests that I should get a flat-file matrix of n.impute imputations, presumably with indexing. I'm a bit stuck using alternatives to aregImpute, as neither MICE nor Amelia seem to like my dataset, and Frank Harrell no longer recommends Transcan for multiple imputations. _______________________________________________________________________ Most doctors use http://www.Doctors.net.uk e-mail. Move to a free professional address with spam a...

...ariates). Now I got my vector of coefficients (and of standard errors). My question is: How could I use directly that vector to run programs such as 'nomogram', 'calibrate', 'validate.cph' which, in contrast, call for the saved results form 'cph' ? I did not use 'aregImpute' for multiple imputation. However, even if I did it, 'fit.mult.impute' seems not to allow specifying the option 'surv=TRUE' (essential to get a nomogram) or 'x=TRUE, y=TRUE' (which are essential for 'calibrate' and 'validate.cph'. Therefore, I dont't...

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

...e timeseries data for some factors, and some missing values are there in those factors, I want impute those missing values without disturbing the distribution of that factor, and maintaining the correlation with other factors. Pl. suggest me some imputation methods. I tried some functions in R like aregImpute, transcan. After the imputation I am unable to retrive the data with imputed values. Please give me some way to get the data with imputed values. Thanks in advance Yours truely Ramesh Kolluru [[alternative HTML version deleted]]

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), as.double(runif(lw)), : C/Fortran function name not in load table When I use "transcan"...

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 tried multiple imputation with aregImpute() and fit.mult.impute() in Hmisc 3.8-3 (June 2010) and R-2.12.1. The warning message below suggests that summary(f) of fit.mult.impute() would only use the last imputed data set. Thus, the whole imputation process is ignored. "Not using a Design fitting function; summary(fit) will use...

...s, just used for join tables), sector_id (categorical variable, for 19 industry sectors), sqft (con't variable for square footage), building_type (categorical, for 2 building types) some values of sqft were inputed wrong, so i'd like to set sqft<1 to "NA" and then use aregImpute() to impute those NAs. Problem: the origianl dataset(.csv format) is too large. though i could read that dataset into R, i could not get aregImpute() run even i set the memory limit to 3G ! (yes, i did the switch in windows to reach 3G rather than 2G) Goal: try to find a way to slim do...

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 c...

Dear R-help, I am trying to impute missing data for the first time using R. The norm package seems to work for me, but the missing values that it returns seem odd at times -- for example it returns negative values for a variable that should only be positive. Does this matter in data analysis, and/or is there a way to limit the imputed values to be within the minimum and maximum of the actual

I'm looking for a package that has a start-of-the-art method of imputation of missing values in a data frame with both continuous and factor columns. I've found transcan() in 'Hmisc', which appears to be possibly suited to my needs, but I haven't been able to figure out how to get a new data frame with the imputed values replaced (I don't have Herrell's book). Any

Dear alltogether, I experience very strange behavior of imputation of NA's with the NORM library. I use R 2.2.0, win32. The code is below and the same dataset was also tried with MICE and aregImpute() from HMISC _without_ any problem. The problem is as follows: (1) using the whole dataset results in very strange imputations - values far beyond the maximum of the respective column, > 200%! and this is reproducible and true for the whole set of imputed NAs (2) using just part (i.e. columns...