similar to: New package: simstudy

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

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

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 <-

Kai, Apologies for the double message, it didn't go to the list last time. On Thu, Oct 22, 2015 at 9:59 AM, Kai Nitschke < kai.nitschke at uniklinik-freiburg.de> wrote: > > ?.Call? calls the C/C++ function ?C_LogLin?. But when I am running it line > by line I get > the following error on line 23/24: > Error: object 'C_LogLin' not found > > Hence, my

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. 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

---1149173172-1804289383-983267779=:26068 Content-Type: TEXT/plain; charset=us-ascii Hello, and a big thank you for providing R! Please find attached a diff for coplot which you may want to consider for the next release. The diff is against R 1.2.2. The reasons for this patch are: 1. The boxes of coplot did not align very well with the panel graphs if applied to a factor 2. Putting the

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

Quoting from Thomas's message - > "On the question of which system really is easier to learn I can > only comment that this isn't the only question where education, > as a field, would benefit from some good randomized controlled > trials." A Randomized Controlled Trial?: Doing such trials would be a 30-year project. The entry criterion might be at

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)

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

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]]

Hello! I have compiled Asterisk 1.8.1.1 on my SheevaPlug. It works all right for me, except for one problem that I have encountered: I can only register a SIP client (X-Lite in my case) if the secret field of the extension is left blank. Otherwise it throws a 'bad auth' error. Does anybody have any clue? -- Arik Goldfeld -------------- next part -------------- An HTML attachment was

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

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 R-devel list, I realized that removing a predictor in lm through the "-"'s operator in formula() does not affect the complete cases that are considered. A minimal example is: summary(lm(Wind ~ ., data = airquality)) # 42 observations deleted due to missingness summary(lm(Wind ~ . - Ozone, data = airquality)) # still 42 observations deleted due to missingness, even if only 7

Dear all, Trying to test for heteroskedasticity I tried several test from the car package respectively lmtest. Now that they produce rather different results i am somewhat clueless how to deal with it. Here is what I did: 1. I plotted fitted.values vs residuals and somewhat intuitively believe, it isn't really increasing... 2. further I ran the following tests bptest (studentized

I have the R code at the end. The last command gives me "1 observation deleted due to missingness". I don't understand what this error message. Could somebody help me understand it and how to fix the problem? > summary(afit) Df Sum Sq Mean Sq F value Pr(>F) A 2 0.328 0.16382 0.1899 0.82727 B 3 2.882 0.96057 1.1136 0.34644 C

Dear R-List, My questions concerns missing values. Specifically, is is possible to use different "types" of missingness in a dataset and not a one-size-fits-all NA? For example, data may be missing because of an outright refusal by a respondent to answer a question, or because she didn't know an answer, or because the item simply did not apply. In later analysis it is sometimes