similar to: Rubin's rules of multiple imputation

So many matrices are square symmetrical (i.e. variance-covariance matrices), is there any way to get R to split the matrix on its diagonal and just return one diagonal? So if I have mat<-matrix(c(1,4,3,4,1,2,3,2,1), nrow = 3, ncol=3, byrow=TRUE) is there anyway to get the lower right diagonal instead of the entire symmetric matrix? -------------------------------------------

Is there a way to get confidence intervals around an ordered regression like polr() in the MASS package? ------------------------------------------- Joe King, M.A. Ph.D. Student University of Washington - Seattle 206-913-2912 <mailto:jp@joepking.com> jp@joepking.com ------------------------------------------- Ad astra per aspera - "Through hardships to the stars"

Hi, all, I'm running multiple imputation to handle missing data and I'm running into a problem. I can generate the MI data sets in both amelia and the mi package (they look fine), but I can't figure out how to get pooled results. The examples from the mi package, zelig, etc., all seem to go right to something like a regression, though all I want are the mean and SE for all the

Hello, I am running a model with four categories and want predicted probabilities in each category. Now for this example I wont give a counterfactual just the training data is fine but is there anyway to get a confidence interval around the predicted probabilities in each group? I have tried but it gives me probabilities and I have used interval="confidence", level=.095 and then interval

I'm trying to use either mice or norm to perform multiple imputation to fill in some missing values in my data. The data has some missing values because of a chemical detection limit (so they are left censored). I'd like to use MI because I have several variables that are highly correlated. In SAS's proc MI, there is an option with which you can limit the imputed values that are

Hi I have used the amelia command from the Amelia R package. this gives me a number of imputed datasets. This may be a silly question, but i am not a statistician, but I am not sure how to combine these results to obtain the imputed dataset to usse for further statistical analysis. I have looked through the amelia and zelig manuals but still can not find the answer. This maybe because I dont

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 R users and developers, I have a dataset containing 34 variables measured in a survey, which has some missing items. I would like to conduct a factor analysis of this data. I tested mi, Amelia, and MissForest as alternative packages in order to impute the missing data. I now have 5 separate datasets with the variables I am interested in factor analysing. In my reading of the package

I have a loop that I would like to use to extract the "stddev" for each itteration so I can average the "stddev" for all the runs. It would be helpful to know how to extract the "stddev" for each run from the VarCorr. Thanks MCruns<-1000 sighatlvec<-rep(NA,MCruns) sighatbvec<-rep(NA,MCruns) sighatevec<-rep(NA,MCruns) for(mc in 1:MCruns) {

Ok so if I have a function: functest<-function(x){ a<-x+1 b<-x+2 c<-x+3 paste(a) paste(b) paste(c) } Now I know I can do cat(), or return() on one of them but if I was to run the function with any number, how could I create objects to save so I could do, I am wondering if I have more than one object within my function. functest$a

Dear R-group, Could somebody recommend a package that can deal with a multinomial response variable (choice of breeding tactic in mice, which has four unordered levels), multiply-imputed data (generated using the Amelia package) and two non-nested random effects: individual identity (133 individuals made up to four choices each) and year (for which there are six levels and sample size varies

Hi everybody, I am quite new to data imputation, but I would like to use the R package ' Amelia II: A Program for Missing Data '. However, its unclear to me how the input for amelia should look like: I have a data frame consisting of numerous coulmns, which represent different experimental conditions, whereby each column has 3 replicates. I want amelia to perform an imputation across

Hi all, I appreciate the help this list has given me before. I have a question which has been perplexing me. I have been working on doing a Bayesian calculating inserting studies sequentially after using a non-informative prior to get a meta-analysis type result. I created a function using three iterations of this, my code is below. I insert prior mean and precision (I add precision manually

I wasn't sure about the subject so I am sorry about the vagueness, but if I have a vector of values, how do I get a certain type of vectors to be bold or italics? So let x be a vector from -10 to 10, how would I tell it to bold the x values that are greater than 0? Or like absolute value of 5? Is it possible to do this in like an if else statement? Could I use x<-seq(-10,10,1)

Hi all, in R I have?Sys.timezone() function to get the current working Time zone. However I want to have a vector to get the list of all available time zones, like say, LETTERS gives me all letters. Is there any function like that? Thanks in advance,

I am wanting to plot a 95% confidence band using segplot, yet I am wanting to have groups. For example if I have males and females, and then I have them in different races, I want the racial groups in different panels. I have this minor code, completely made up but gets at what I am wanting, 4 random samples and 4 samples of confidence, I know how to get A & B into one panel and C&D in to

Hi, I am a long time SPSS user but new to R, so please bear with me if my questions seem to be too basic for you guys. I am trying to figure out how to analyze survey data using logistic regression with multiple imputation. I have a survey data of about 200,000 cases and I am trying to predict the odds ratio of a dependent variable using 6 categorical independent variables (dummy-coded).

Hi all, after using Amelia II to create 10 imputed data sets I need to average them to have one unique data that includes the average for each cell of the variables imputed, in addition to the values for the variables not imputed. Such data has many variables (some numeric, other factors), and more than 20000 observations. I do not know how to average them out. Any help? Below I provide a small

Hi list, I am wondering if there is a way to use EM algorithm to handle missing data and get a completed data set in R? I usually do it in SPSS because EM in SPSS kind of "fill in" the estimated value for the missing data, and then the completed dataset can be saved and used for further analysis. But I have not found a way to get the a completed data set like this in R or SAS. With

Hi list, I am wondering if there is a way to use EM algorithm to handle missing data and get a completed data set in R? I usually do it in SPSS because EM in SPSS kind of "fill in" the estimated value for the missing data, and then the completed dataset can be saved and used for further analysis. But I have not found a way to get the a completed data set like this in R or SAS. With