search for: imputation

Displaying 20 results from an estimated 532 matches for "imputation".

2013 Feb 15
0
Ho w Do I Get Cox Model Convergence After Multiple Imputation
Due to missing data with some of my predictor variables I first do multiple imputation as follows: library(foreign) library(Amelia) library(norm) set.seed(666) M=10 impdat <- NVP[,c("X_t0","X_t","nvp","adstatus","t0rwfa","ageatran","whostage","t0rhfa","vlsupp","t0rwfh","t0rvl&...
2008 Oct 29
1
Help with impute.knn
ear all, This is my first time using this listserv and I am seeking help from the expert. OK, here is my question, I am trying to use impute.knn function in impute library and when I tested the sample code, I got the error as followingt: Here is the sample code: library(impute) data(khanmiss) khan.expr <- khanmiss[-1, -(1:2)] ## ## First example ## if(exists(".Random.seed"))
2010 Aug 10
1
Multiple imputation, especially in rms/Hmisc packages
Hello, I have a general question about combining imputations as well as a question specific to the rms and Hmisc packages. The situation is multiple regression on a data set where multiple imputation has been used to give M imputed data sets. I know how to get the combined estimate of the covariance matrix of the estimated coefficients (average the M covar...
2005 May 04
3
Imputation
  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 values. Please give me some way to get the data with imputed values. Thanks in advance Yours truely Ramesh Kolluru [[alternative HTML version deleted]]
2008 Nov 26
1
multiple imputation with fit.mult.impute in Hmisc - how to replace NA with imputed value?
I am doing multiple imputation with Hmisc, and can't figure out how to replace the NA values with the imputed values. Here's a general ourline of the process: > set.seed(23) > library("mice") > library("Hmisc") > library("Design") > d <- read.table("DailyDataRaw...
2011 Jun 23
2
Rms package - problems with fit.mult.impute
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 I get a warning message when I do that with fit.multiple.impute. a<-aregImpute(~med.hist....
2008 Feb 11
1
Help with write.csv
...d in csv file. I have followed the example in the impute package as follows: > mydata = read.csv("sample_impute.csv", header = TRUE) > mydata.expr <- mydata[-1,-(1:2)] > mydata.imputed <- impute.knn(as.matrix(mydata.expr)) The impute is succesful. Then I try to write the imputation results (mydata.imputed) to a csv file such as follows.. > write.csv(mydata.imputed, file = "sample_imputed.csv") Error in data.frame(data = c(-0.07, -1.22, -0.09, -0.6, 0.65, -0.36, 0.25, : arguments imply differing number of rows: 18, 1, 0 I need help understanding the error mess...
2012 Dec 08
1
imputation in mice
...he glm function: ps.model = glm(assignment~totalexp + yrschool+new+cert+age+STratio+percminority+urbanicity+povproblem+numthreats+numbattack, family = "binomial", data = data) data$propensityscores = fitted(ps.model) In each case, I have tried running the code after having performed zero imputations, 1 imputation, and 5 imputations. A colleague looked at my code and assured me that I was doing the imputations correctly. However, even after performing the imputation, one of the continuous variables still has NAs. This is the code that I am using for 5 imputations: library(mice) #Remove weig...
2006 Mar 24
0
Imputing NAs using transcan(); impute()
Dear all, I'm trying to impute NAs by conditional medians using transcan() in conjunction with impute.transcan(). ... see and run attached example.. Everything works fine, however impute() returns saying Under WINDOWS > x.imputed <- impute(trans) Fehler in assign(nam, v, where = where.out) : unbenutzte(s) Argument(e) (where ...) Zus?tzlich: Warnmeldung: variable X1 does not
2011 Feb 07
1
multiple imputation manually
Hi, I want to impute the missing values in my data set multiple times, and then combine the results (like multiple imputation, but manually) to get a mean of the parameter(s) from the multiple imputations. Does anyone know how to do this? I have the following script: y1 <- rnorm(20,0,3) y2 <- rnorm(20,3,3) y3 <- rnorm(20,3,3) y4 <- rnorm(20,6,3) y <- c(y1,y2,y3,y4) x1 <- 1+2*y1+ rnorm(20,0,8) x2 <- 1...
2008 Jun 30
3
Is there a good package for multiple imputation of missing values in R?
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 Herr...
2003 Jul 27
1
multiple imputation with fit.mult.impute in Hmisc
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 5. # defaultLinear makes sense for our data. fmp <- fit.mult.impute(Y ~ X1 + X2 ... [for the model of inte...
2011 Mar 31
2
fit.mult.impute() in Hmisc
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(fi...
2007 Sep 26
1
using transcan for imputation, categorical variable
Dear all, I am using transcan to impute missing values (single imputation). I have several dichotomous variables in my dataset, but when I try to impute the missings sometimes values are imputed that were originally not in the dataset. So, a variable with 2 values (severe weight loss or no/limited weight loss) for example coded 0 and 1, shows 3 different values after imp...
2011 Dec 02
2
Imputing data
So I have a very big matrix of about 900 by 400 and there are a couple of NA in the list. I have used the following functions to impute the missing data data(pc) pc.na<-pc pc.roughfix <- na.roughfix(pc.na) pc.narf <- randomForest(pc.na, na.action=na.roughfix) yet it does not replace the NA in the list. Presently I want to replace the NA with maybe the mean of the rows or columns or
2009 Jan 23
0
Package impute exist in quite different version on CRAN and BioC
[CC:ing package maintainer of 'impute' package and crossposting to r-devel and bioc-devel because this affects both audiences] Hi, the 'impute' package is published both on CRAN and Bioconductor; http://cran.r-project.org/web/packages/impute/ http://bioconductor.org/packages/2.3/bioc/html/impute.html The one on CRAN is v1.0-5, and the one on BioC is v1.14.0. The two
2003 Dec 08
1
Design functions after Multiple Imputation
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', 'validate.cph' which, in contrast, call for the saved results form '...
2013 Jan 14
0
Changing MaxNWts with the mi() function (error message)
...ting the variable, "sex," the mi() function accesses the mi.categorical() function, which then accesses the nnet() function. I then receive the following error message (preceded by my code below): > imputed.england=mi(england.pre.imputed, n.iter=6, add.noise=FALSE) Beginning Multiple Imputation ( Mon Jan 14 13:39:49 2013 ): Iteration 1 Chain 1 : sex Error while imputing variable: sex , model: mi.categorical Error in nnet.default(X, Y, w, mask = mask, size = 0, skip = TRUE, softmax = TRUE, : too many (3432) weights The error message indicates that there are too many weights (3432)....
2006 Sep 25
2
Multiple imputation using mice with "mean"
Hi I am trying to impute missing values for my data.frame. As I intend to use the complete data for prediction I am currently measuring the success of an imputation method by its resulting classification error in my training data. I have tried several approaches to replace missing values: - mean/median substitution - substitution by a value selected from the observed values of a variable - MLE in the mix package - all available methods for numerical data in t...
2011 Jun 08
1
install the “impute” package in unix
Hi, I am trying to install the “impute” package in unix. but I get the following error message. I followed the following steps. Do you know what is causing this and how I can solve this problem? source("http://www.bioconductor.org/biocLite.R") biocLite("impute") Using R version 2.11.1, biocinstall version 2.6.10. Installing Bioconductor version 2.6 packages: [1]