similar to: Hnadling missing data In R

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

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

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)

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) x3 <- rnorm(1000) y <- x2 +

>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 >

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,

Dear list, I'd like to use multiple imputations to try and save a somewhat badly mangled dataset (lousy data collection, worse than lousy monitoring, you know that drill... especially when I am consulted for the first time about one year *after* data collection). My dataset has 231 observations of 53 variables, of which only a very few has no missing data. Most variables have 5-10% 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

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 i'm finding it difficult to open (because its

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 am a new user of R for Windows, enthusiast about the many functions of the Design and Hmisc libraries. I combined the results of a Cox regression model after multiple imputation (of missing values in some covariates). 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',

Hello, I am using R.12.2.0. I am trying to generate bootstrap confidence intervals using bootcov from the rms package. I am able to impute the missing data using aregImpute and to perform a linear regression on the imputed datasets using fit.mult.impute, but I am unable to use bootcov to generate the confidence intervals for the R-squared. Here is a small example that should duplicate the

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

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 standard errors, t, P from last imputation only. Use

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

Hello List, i have been agonizing over this for days, any reply would be greatly appreciated! Situation:___________________________________ My original dataset is a .csv dataset (w/ 2M records) with 4 variables: job_id (Primary key, won't be used for analysis, just used for join tables), sector_id (categorical variable, for 19 industry sectors), sqft (con't variable for square

  I have 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

First, many thanks to Frank Harrell for once again helping me out. This actually relates to the next point, which is my contribution to the 'why don't social scientists use R' discussion. I am a hybrid social scientist(child psychiatrist) who trained on SPSS. Many of my difficulties in coming to terms with R have been to do with trying to apply the logic underlying SPSS, with dire

Hi! Does anyone know how to do the test for goodness of fit of a logistic model (in rms package) after running fit.mult.impute? I am using the rms and Hmisc packages to do a multiple imputation followed by a logistic regression model using lrm. Everything works fine until I try to run the test for goodness of fit: residuals(type=c("gof")) One needs to specify y=T and x=T in the fit. But

Hi, I have a survey dataset of about 20000 observations where for 2 factor variables I have about 200 missing values each. I want to impute these using 10 possibly explanatory variables which are a mixture of integers and factors. Since I was quite intrigued by the concept of rrp I wanted to use it but it takes ages and terminates with an error. First time it stopped complaining about too little