similar to: Multiple imputation on subgroups

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 <-

I'm sure there's a simple way to do this, but I can only think of complicated ones. I have a number of character vectors that look something like this: "12 78 23 9 76 43 2 15 41 81 92 5(92 12) (81 78 5 76 9 41) (23 2 15 43)" I wish to get it into a list of numerical vectors like this: $Group [1] 12 78 23 9 76 43 2 15 41 81 92 5 $Subgroup1 [1] 92 12 $Subgroup2 [1] 81 78 5

Hi all, How can I calculate the mean from several imputed data sets with the package mice? I know you can estimate regression parameters with, for example, lm and subsequently pool those parameters to get a point estimate using functions included in mice. But if I want to calculate the mean value of a variable over my multiple imputed data sets with fit <- with(data=imp, expr=mean(y)) and

Hello, I'm using the rmeta package to perform a meta analysis using summary statistics rather than raw data, and would like to analyze the effects in three different subgroups of my data. Furthermore, I'd like to plot this on one forest plot, with corresponding summary weighted averages of the effects displayed beneath each subgroup. I am able to generate the subgroup analyses by simply

# Dear all, # how to replace values in x by means in subgroups created in ... # replace only these values where y=0 in continous sequence # replace by mean calculated locally for each subgroup created by # continous sequence of 0,0,0 in parallel y vector, i.e. # where there is continous sequence of 0 in data frame vector y # but we do not replace values in x[i], if y[i]!=0 # we do not want

Hello. I have run the command imp<-mice(mydata, im=c("","pmm","logreg","logreg"),m=5)  for a variable with no missing data, a numeric one and two variables with binary data. I got the following message: There were 37 warnings (use warnings() to see them) > warnings() Warning messages: 1: In any(predictorMatrix[j, ]) ... : coercing argument of

Hi All, Is there a feasibility for creating subgroups on the Puppet Dashboard? Basically the requirement is that we have a huge number of VMs running designated services. Some of VMs having the same service may yet have different properties configured on them. We are exploring the feasibility of having these properties managed through puppet. Currently all these VMs are put into a single

Hi folks, I have a dataframe df.vars with the follwing structure: var1 var2 var3 group Group is a factor. Now I want to standardize the vars 1-3 (actually - there are many more) by class, so I define z.mean.sd <- function(data){ return.values <- (data - mean(data)) / (sd(data)) return(return.values) } now I can call for each var z.var1 <- by(df.vars$var1, group,

I'm hoping there is a simple answer to this - it seems that there should be, but I can't figure it out. I have a matrix/data frame with three variables of interest - V1, V2, V3. One, V1, is a factor with x levels (x may be a large number); I want to calculate the correlation between the other two (i.e. cor(V2,V3)) for each level, and store it as a vector of length x. I should think this

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

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

  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

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

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

Hi all, I have a question about multiple imputation within the MICE package. I want to use passive imputation for my variable called X, because it is calculated out of multiple variables, namely Y, Z. Let's give an example with BMI. I know, that if I want to use passive imputation for BMI, I can use the following command: meth["BMI"] <- "~I(weight/(height/100)^2)"

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

Dear Members of the R-Help, While using a R function - 'aggregate' that you developed, I become to have a question. In that function, > aggregate(x, by, FUN, ..., simplify = TRUE) I was wondering about what type of FUN I should write if I want to get "the most frequent value of the subgroup" as a summary statistics of the subgroups. I will appreciate if I can get

I am working with some survival data with missing values. I am using the mice package to do multiple imputation. I have found code in this thread which handles pooling of the MI results: https://stat.ethz.ch/pipermail/r-help/2007-May/132180.html Now I would like to plot a survival curve using the pooled results. Here is a reproducible example: require(survival) require(mice) set.seed(2) dt

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

Is there a good way in R to impute values which exist, but are less than the detection level for an assay? Thanks, Jonathan Williams OPTIMA Radcliffe Infirmary Woodstock Road OXFORD OX2 6HE Tel +1865 (2)24356