similar to: Covariance of data with missing values.

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 <-

Hi. Can anyone point me to any module in R which implements "Little's Chi Square test" for MCAR. The problem is that i have around 60 behavioural variables on a 6 point categorical scale which i need to test for MCAR and MAR. What i can make out from preliminary analysis is that moderate (0.30 to 0.60) correlations may be present in several variable pairs leading me to suspect

A colleague of mine is trying to use nls() to effect an optimization, and is encountering a scoping problem. I should know how to solve it for him but .... well, I just don't. I also had a quick scrounge of the archives --- I know I've seen this topic addressed before --- but I couldn't track it down. So here's a toy example that demonstrates the problem: hhh <-

I'm relatively new to R, and not particularly adept yet, but I was wondering if there was a simply way to simulate missing data that are MAR, MNAR and MCAR. I've got a good work-around for the MCAR data, but it's sort of hard to work with. Josh [[alternative HTML version deleted]]

Dear all I want to create dataset with MCAR type from my dataset. I have my dataset with 100 records, and I want to create dataset from this dataset to missing 5 records. How I can do it. THX Jumlong -- Jumlong Vongprasert Institute of Research and Development Ubon Ratchathani Rajabhat University Ubon Ratchathani THAILAND 34000 [[alternative HTML version deleted]]

Dear all, I am trying to use the GEE methodology to fit a trend for the number of butterflies observed at several sites. In total, there are 66 sites, and 19 years for which observations might be available. However, only 326 observations are available (instead of 1254). For the time being, I ignore the large number of missing values, and the fact that GEE is only valid under MCAR. When I run the

On 7/28/2016 11:04 PM, Peter wrote: > On 29/07/16 17:13, geo.inbox.ignored wrote: >>> >>the Semperon SI-42 is, I believe, a 64 bit capable CPU, >> > >> >you are very correct. > In that case stop wasting your time with CentOS 7 i386 and install the > native x86_64 version of CentOS 7. This will give you access to all the > 3rd party repos you'll

Hi there, I have subscribed to R-help but am not sure how to view or post questions? I think this is the right way. I am planning on doing a multivariate regression investigating the relationship between depression (a continuous variable) and social support variables (mostly continuous, some categorical) among older people. I have a number of demographic and health-related variables that I am

On Dec 3, 2014, at 12:01, Greg Bailey wrote: > I think you'd need to use the "isohybrid" command that's included in the syslinux package. BING! BING! BING! We have a winner! Thanks for that info, it makes the USB stick bootable. This will make kickstart installations so much easier, especially for systems that don?t have a CD-ROM (we used to have to scrounge for an

Dear R-users, I would like to compute a robust covariance matrix of two series of realizations of random variables: ###Begin Example### data <- cbind(rnorm(100), rnorm(100)) model <- lm(data ~ 1) vcov(model) library(sandwich) NeweyWest(model) #produces an error ###End Example### NeweyWest() produces an error but sandwich(), vcovHAC(), kernHAC, weave(),... do not produce any errors. It

Hi, I am running a simple linear model with (say) 5 independent variables. Is there a simple way of getting the variance-covariance matrix of the coeffcient estimates? None of the values of the lm() seem to provide this. Thanks in advance, Ritwik Sinha rsinha@darwin.cwru.edu Grad Student Case Western Reserve University [[alternative HTML version deleted]]

I've looked through the FAQ and can't find anything about this. I'm working on a Sun machine, running Solaris 2.7. I'm a new R-user; have been using Splus for millenia, and finally decided it was time I got going with R. So I downloaded R, installed it, fiddled about a bit, and found that command-line editing was absent. Scrounging through the documentation some more, I saw it

I've got a system (Supermicro P8SCT) that lacks a floppy or CD and it's old enough that it won't boot from a USB stick. Before I scrounge an optical drive to plug in, I thought perhaps I could install over the network, as I see "legacy LAN boot" in the BIOS menu. The guides for a LAN install I've found all want to do an automated kickstart install. Are there any

Dear all, I have a simple question which I unfortunately do not seem to be able to solve myself. I have a (NxK) matrix and want to generate a new matrix by multiplying each row with itself such that the new matrix has dimension ((N*K)xK) (or better, generate an array with dimension (K,K,N)). I tried apply, but that did not work. Any suggestions? Thanks! Stephan ## Here is a simple

What R functionnalities are there to do missing values imputation (substantial proportion of missing data)? I would prefer to use maximum likelihood methods ; is the EM algorithm implemented? in which package? Thanks Anne ---------------------------------------------------- Anne Piotet Tel: +41 79 359 83 32 (mobile) Email: anne.piotet@m-td.com

L.S., Does anyone know if there is an R library which implements Little's MCAR test for completely at random missing values? It is implemented in SPSS and SAS, and widely mentioned in the literature. Thanks in advance! Sander van Kuijk -- View this message in context: http://r.789695.n4.nabble.com/Little-s-MCAR-test-tp2331137p2331137.html Sent from the R help mailing list archive at

Anybody knows what functions can be used to calculate variance/covariance with complex numbers? var and cov don't seem to work: > a 1 V1 0.00810014+0.00169366i V2 0.00813054+0.00158251i V3 0.00805489+0.00163295i V4 0.00809141+0.00159533i V5 0.00813976+0.00161850i > var(a) 1 1 1.141556e-09 Warning message: In var(a) : imaginary parts discarded in

Hello!   I would like to sample 30 % of cases (with at least 1 value lower than 3) and among them I want to set all values lower than 3 (within selected cases) as NA (NMAR- Not missing at random). I managed to sample cases, but I don’t know how to set values (lower than 3) as NA.   R code:   x <-

Dear all, I want to do a logistic regression. So far I've only found out how, in a dataset of complete cases. I'd like to do logistic regression via max likelihood, using all the study cases (complete and incomplete). Can you help? I'm using glm() with family=binomial(logit). If any covariate in a study case is missing then the study case is dropped, i.e. it is doing a complete case

I'm trying to make multiple line plots, each with a different color, using the xyplot command. Specifically, I have an NxK matrix Y and an Nx1 matrix x. I would like the plot to contain a line for each (x, Y[,i]), i=1:K. I know something like xyplot(Y[,1] + Y[,2] + Y[,3] ~ x, type='l') will work, but if Y is large, this notation can get very awkward. Is there a way to do something