search for: mcar

...o 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 <- 1+2*y1+ rnorm(20,0,8) x2 <- 1+2*y2+ rnorm(20,0,8) x3 <- 1+2*y3+ rnorm(20,0,8) x4 <- 1+2*y4+ rnorm(20,0,8) x <- c(x1,x2,x3,x4) mcar.y <- rep(NA,80) y.mis <- rep(NA,80) df <- data.frame(y=y, y.mis=y.mis, mcar.y=mcar.y, x=x) df$y.mis <- df$y for (j in 1:80) { df$mcar.y <- rbinom(80,1,0.15) } ind0 <- which(df$mcar.y==0) ind1 <- which(df$mcar.y==1) if (length(ind0) > 68) { df$mcar.y[sample(ind0, length(in...

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 that the d...

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...

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 [[alterna...

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]]

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]]

...ous, some categorical) among older people. I have a number of demographic and health-related variables that I am including as control variables. I have a large dataset from nearly 4,000 individuals. I need to check whether my data is 1) Missing at Random (MAR) and 2) Missing Completely At Random (MCAR). Here are three questions that I have related to this: 1) To check whether the data is MAR, I dichotomised a variable into missing and not missing, and checked for any significant differences in means (for continuous) or proportions (for categorical) of the other variables. I did this for each...

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

...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 following code geeglm(SumOfButterflies ~ RES_YEAR, family = poisson, data = ManijurtNoNA, id = RES_ROTE_ID, corstr = "ar1") I obtain "normal" output. Not surprisingly, overdispersion is present (Estimated Scale Parameters: [1] 185.8571), so changing to quasipo...

...atrix ``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) contain one or more missing values. If I am willing to assume that missing entries are missing completely at random (MCAR) then I can estimate the covariance matrix Sigma via maximum likelihood, by employing the EM algorithm. Or so I believe. Has this procedure been implemented in R in an accessible form? I've had a bit of a scrounge through the searching facilities, and have gone through the FAQ, and hav...

...as a csv file), and generates data based on these specifications. The final data sets can represent data from randomized control trials, observed (non-randomized) studies, repeated measure (longitudinal) designs, and cluster randomized trials. Missingness can be generated using various mechanisms (MCAR, MAR, NMAR). Currently, data can be generated from normal/Gaussian, binary, Poisson, truncated Poisson, Gamma, and uniform distributions. Survival data can also be generated. I will be adding functionality over time, and will be particularly interested in knowing what userRs would be interested in...

...as a csv file), and generates data based on these specifications. The final data sets can represent data from randomized control trials, observed (non-randomized) studies, repeated measure (longitudinal) designs, and cluster randomized trials. Missingness can be generated using various mechanisms (MCAR, MAR, NMAR). Currently, data can be generated from normal/Gaussian, binary, Poisson, truncated Poisson, Gamma, and uniform distributions. Survival data can also be generated. I will be adding functionality over time, and will be particularly interested in knowing what userRs would be interested in...

Greetings, The attached code is my preliminary attempt to create a comprehensive missing data diagnostic package. I would greatly appreciate any constructive feedback about the code and encourage others to use it if they feel it is helpful. One warning might be in order - I have tested the mcar test only superficially. There might be a few other bugs but it mostly works for my initial purposes. I intend to create a complete package with the proper documentation shortly. Apologies in advance if this is not the proper forum to post code but I did not see anything in the FAQ's or de...

Dear R-users I've simulated a longitudinal multivariate normal data set from which I've simulated missing-patterns such as MCAR MAR and a simple kind of non-MAR. I've imputated the values so I now have 'complete' data sets. I'm trying to perform a T2-test as done in the multivariate case under th enormal assumption. Is there something I've to think about when performing this test on a longitudinal multiv...

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     ####percent of missing v...

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 point in the right direction. /Mauri [[alternative HTML version deleted]]

R users: Thanks in advance  How to generate missing at random (MAR)? assaedi76@yahoo.com Thanks [[alternative HTML version deleted]]

Dear all, I have been attempting to use multiple imputation (MI) to handle missing data in my study. I use the mice package in R for this. The deeper I get into this process, the more I realize I first need to understand some basic concepts which I hope you can help me with. For example, let us consider two arbitrary variables in my study that have the following missingness pattern: Variable 1

...RR0 0000000000000000 MCSRR1 0000000000000000 TCR 0000000000000000 TSR 0000000000000000 ESR 0000000000000000 DEAR 0000000000000000 PIR 0000000000000000 DECAR 0000000000000000 IVPR 0000000000000000 EPCR 0000000000000000 MCSR 0000000000000000 SPRG8 0000000000000000 EPR 0000000000000000 MCAR 0000000000000000 PID1 0000000000000000 PID2 0000000000000000 SVR 0000000000000000 MAS0 0000000000000000 MAS1 0000000000000000 MAS2 0000000000000000 MAS3 0000000000000000 MAS4 0000000000000000 MAS6 0000000000000000 MAS7 0000000000000000 PID 0000000000000000 MMUCFG 0000000000000000 TLB0CFG...