search for: nmar

...glin? 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, dtab, conf, table, start, nmar, eps, iter) ?.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 questions: 1. Where does the original ?loglin? function get the ?C_LogLin? function from (some libraries...

...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, dtab, conf, table, start, nmar, eps, iter) > > ?.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 questions: > 1. Where does the original ?loglin? function get the ?C_Log...

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 <- matrix(c(1,2,3,4,5,1,2,3,4,5,1,2,3,4,5,1,2,3,4,5,1,2,3,4,5,1,2,3,4,5,1,2,3,4,5,1,2,3,4,5,1,2,3,4,5,3,3,3,4), nrow = 7, ncol=7, byrow=TRUE) ####matrix   pMiss <- 30 ...

...! axis(2, xpd = NA) else if ((j == columns || index == nplots) && ((total.rows - ! i)%%2 == 1)) ! axis(4, xpd = NA) box() } if (have.b) { *************** *** 255,274 **** par(fig = c(0, f.col, f.row, 1), mar = nmar, new = TRUE) plot.new() nint <- nrow(a.intervals) ! pwoffs <- nint / 32 ! plot.window(c(min(a.intervals[is.finite(a.intervals)] + pwoffs), ! max(a.intervals[is.finite(a.intervals)]) - pwoffs), ! 0.5 + c(0, nint), lo...

...ile), 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 having me...

...ile), 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 having me...

...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]]

...erms in the model, e.g., 1+2+3, so there is no way AFAICS to specify an intercept-only model. That is, below, the model ~1 is actually the saturated model for the one-way table. > loglm(~NULL, t1) Error in denumerate.formula(formula) : node stack overflow > loglm(~0, t1) Error in double(nmar) : vector size cannot be NA/NaN > loglm(~1, t1) Call: loglm(formula = ~1, data = t1) Statistics: X^2 df P(> X^2) Likelihood Ratio 0 0 1 Pearson 0 0 1 > -- Michael Friendly Email: friendly AT yorku DOT ca Professor, Psychology Dept. &a...

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