similar to: Not missing at random

Hello! I would like to sample 5 % of cases and from 1 to 3 variables within selected cases and set them as NA (MCAR-Missing completely at random). I managed to sample cases and variables, but I don’t know how to set them as NA. R code: N <- 1000      ####number of cases n <- 12           ####number of variables X <- matrix(rnorm(N * n), N, n)    ####matrix pMiss <- 0.05    

Dear R buddies, I’m trying to run Principal Component Analysis, package princomp: http://stat.ethz.ch/R-manual/R-patched/library/stats/html/princomp.html. My question is: why do I get different results with pca = princomp (x, cor = TRUE) and pca = princomp (x, cor = FALSE) even when I standardize variables in my matrix? Best regards, Blaž Simčič [[alternative HTML version deleted]]

Dear all, I would like to generate 500 matrices of 20 numbers from standard normal distribution with names x1,x2,x3,….x500.   I tried with loop for, but I don’t know how to name matices : for (i in 1:500)  {    x[[i]] <- matrix(rnorm(20), 4)     } Any suggestion? Thanks,  Blaž [[alternative HTML version deleted]]

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

Hi guys, I want to transfer the following code from R into .Call compatible form. How can i do that? Thanks!!! INT sim; for(i in 1:sim){ if(i>2) genemat <- genemat[,sample(1:ncol(genemat))] ranklist[,1] <- apply(genemat, 1, function(x){ (mean(x[cols]) - mean(x[-cols]))/sd(x)}) ranklist <- ranklist[order(ranklist[,1]),]

Dear mailing list, I have a C function that gives me a wrong result when I run it under Windows 7. This is the code under Linux (RHEL5): > library(phenoTest) > data(epheno) > sign <- sample(featureNames(epheno))[1:20] > score <- getFc(epheno)[,1] > head(score) 1007_s_at 1053_at 117_at 121_at 1255_g_at 1294_at -1.183019 1.113544 1.186186 -1.034779 -1.044456

Hi everyone, I have a question regarding a C function of the "stats" package in R. I tried to understand the ?loglin? basic function of the ?stats? package implemented in R. The implemented function itself runs without any problem (perhaps see sample). When I ran it line by line it stopped at the lines 23-24 of the loglin-function; (the following line): z <- .Call(C_LogLin,

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

Hello everybody, I've setup a file server with Debian GNU/Linux (Sarge) and Samba v3.0.10 with ACL, Kerberos etc... so I can join the Windows 2000 domain. I can use users from domain on file server without problems and Windows 2000, XP clients can access Samba shares. But when I try to connect to \\fs from Windows 98 client (in domain) it requests password from me (\\fs\IPC$). I have

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]]

When I want to see my users, then I get: ----------------------------------------------------- You have a nil object when you didn''t expect it! You might have expected an instance of Array. The error occured while evaluating nil.each Extracted source (around line #3): 1: <% @page_title = "Spisek uporabnikov" -%> 2: <table> 3: <% for user in @all_users %> 4:

I wish to pass a vector ``y'', some of whose entries are NAs to a fortran subroutine which I am dynamically loading and calling by means of .Fortran(). The subroutine runs through the vector entry by entry; obviously I want to have it do one thing if y[i] is present and a different thing if it is missing. The way I am thinking of proceeding is along the xlines of: ymiss <- is.na(y)

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]]

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

Is there any further development in R to identify the missing data Mechanism? Would you please help ? ======= Please find our Email Disclaimer here-->: http://www.ukzn.ac.za/disclaimer ======= [[alternative HTML version deleted]]

Hello I am trying to get logwatch working on CentOS 8. System is fully updated. Usually install minimal version and then add only necessary with yum. On CentOS 7: install logwatch and get daily logwatch report on mail. On CentOS 8: install logwatch but no way to get mail. Am I doing something wrong? Or miss something? Thanks in advance Blaz

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

I have a data matrix X (n x k, say) each row of which constitutes an observation of a k-dimensional random variable which I am willing, if not happy, to assume to be Gaussian, with mean ``mu'' and covariance matrix ``Sigma''. Distinct rows of X may be assumed to correspond to independent realizations of this random variable. Most rows of X (all but 240 out of 6000+ rows)

Hi I've simulated multivariate longitudinal. The data is a mixture of conitnous and categorical data. I've stored it in matrix form with the time dependent variables as colons. Now I want to create a monote missing data pattern starting of with MCAR-missingnes and different proportions of missingdata and then refine the function to handle MAR and NMAR. Is there anybody that could help or