search for: fweiler08

Dear all, I have a question regarding using JAGS and R. My problem is that every single time I want to call JAGS from R the latter crashes (i.e. it turns pale and the Windows tells me "R has stopped working". Strangely, if there is a mistake in my jags code, this will be reported without a crash. Only when the code is right and the model should start to run, R stops working. I already

Dear all, I am using the book "Generalized Linera Models and Extension" by Hardin and Hilbe (second edition, 2007) at the moment. The authors suggest that instead of OLS models, "the log link is generally used for response data that take only positive values on the continuous scale". Of course they also suggest residual plots to check whether a "normal" linera model

Dear all, I have a question about the lattice package, more specifically about the control of the x-axis length in the different panels. I use the following code to make the stacked barchart: barchart(country ~ climatechangefocalpoint + meteorologyservice + adaptationorvulnerability + cdmcarbonmarkets + energy + aviationmaritimetransport + forestry + pressofficer|period, data=graph5,

Dear all, I have a (probably very basic) question. I am imputing data with the mice package, using 10 chains. I can then write out the 10 final values of the chains simply by name1 <- complete(imp, 1) : : name10 <- complete(imp,10) Not a big deal, I just wanted to do that in a little loop as follows: for (i in 1:10){ set[i] <-

Dear all, I have a problem with my stacked bar charts. I have one very long bar, hence I would like to break the x-axis at a certain point so that the shorter bars can be seen better. Here is a cooked up example: library(lattice) group <- rep(1:3,10) x <- runif(30, 0, 100) y <- runif(30, 0, 100) x[5] <-500 z <- rep(seq(1:10),3) z <- z[order(z)] frame <-

Dear all, I am trying to impute data for a range of variables in my data set, of which unfortunately most variables have missing values, and some have quite a few. So I set up the predictor matrix to exclude certain variables (setting the relevant elements to zero) and then I run the imputation. This works fine if I use predictive mean matching for the continous variables in the data set. When I

Dear all, being a relative beginner in R, I apologize for posting the second question within two days. So I want a stacked barchart, which should look like the one produced by this code: Tuvalu <- c(9,3,4,0,3,0,0) Singapor <- c(38,0,0,0,12,19,0) Samoa <- c(26,16,2,0,5,2,0) PNG <- c(56,4,0,5,2,0,56) Micronesia <- c(6,0,0,0,0,0,0) graph4 <-

Dear all, I am new to network analysis, but since I have good data I started to read about it and learned how to use the ergm and related packages. I generally get interesting results, but when I run a model including sociality and selective mixing effects for different groups, the model runs (and converges) but I get a warning as follows: mod <- ergm(network ~ edges + gwesp(0, fixed=T) +