search for: imputed

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 <-

...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")) rm(.Random.seed) khan.imputed <- impute.knn(as.matrix(khan.expr)) ## ## khan.imputed$data should now contain the imputed data matrix x<-khan.imputed$data Here are the results: > library(impute) > data(khanmiss) > khan.expr <- khanmiss[-1, -(1:2)] > ## ## First example > ## if(exists(".Random....

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 covariance matrices from the individual imputations and add (M+1)/M times the between-imputation covariance matrix), and I know how to use this to get p-values and confidence int...

...actors, 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]]

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_01.txt",header=T) > length(d);length(d[,1]) [1] 43 [1] 2666 Do for this d...

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

Dear all, I am new to R. I am using the impute package with data contained 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,...

...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 weights data$weight<-NULL #perform the imputation imputed.data = mice(data, m = 5, diagnostics = F) #reinsert the weights imputed.data.final=complete(imputed.data) imputed.data.final$weight=lbdata$weight #rename the imputed dataset "data" data = imputed.data.final When I perform optimal propensity score matching or nearest neighbor matching (r...

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 have a names() attribute and data does not have row.names. Assuming row names are integers. in: impute.transcan(trans) > Under LINUX >...

...ave run this imputation-script (for 1 incomplete data set), my data set is already complete. I would like to get back to the incompleted data set used before, and repeat the single imputation process four times with the same incomplete data set (so I can calculate some mean of parameters from the 5 imputed data sets later on). But how? Thanks. -- View this message in context: http://r.789695.n4.nabble.com/multiple-imputation-manually-tp3263786p3263786.html Sent from the R help mailing list archive at Nabble.com.

...t 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 pointers would be appreciated. ================================================================ Robert A. LaBudde, PhD, PAS, Dpl. ACAFS e-mail: ral at lcfltd.com Least Cost Formulations, Ltd. URL: http://lcfltd.com/ 824 Timberl...

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

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 vcov(fit) to get the correct covariance matrix, sqrt(diag(vcov(fit))) to get s.e." But the standard er...

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 imputation (0, 1 and 2). I have tried two options: impfile <- transcan (~age + factor(dumgend) +factor(dumcomorb25_i) +...

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

...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 different versions of impute.knn() returns different structures, e.g. impute v1.0-5: > library(impute) > example(impute.knn) > str(khan.imputed) num [1:2308, 1:63] 0.773 -2.438 -0.483 -2.721 -1.217 ... - attr(*, "dimnames")=List of 2 ..$ : chr [1:2308] "2" "3" "4" "5" ... ..$ : chr [1:63] "sample1" "sample2" "sample3" "sample4" ... - attr(*, &q...

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',

...ata with the mi() function (mi package) and keep receiving an error message. When imputing 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 m...

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

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]