similar to: small issue with densityplot

I'm fitting a GLMM to some questionnaire data. The structure is J individuals, nested within I areas, all of whom answer the same K (ordinal) questions. The model I'm using is based on so-called continuation ratios, so that it can be fitted using the lme4 package. The lmer function fits the model just fine, but using mcmcsamp to judge the variability of the parameter estimates produces

Hi, I have fitted a generalized linear mixed effects model using lmer (library lme4), and the family = quasibinomial. I have tried to obtain a MCMC sample, but on calling mcmcsamp(model1, 1000) I get the following error which I don't understand at all: Error in .local(object, n, verbose, ...) : Update not yet written traceback() delivers: 4: .Call(mer_MCMCsamp, ans, object) 3:

Dear all, I am running a mixed model using lmer. In order to obtain CI of individual coefficients I use mcmcsamp. However, I need advice which values that are most appropriate to present in result section of a paper. I have not used mixed models and lmer so much before so my question is probably very naive. However, to avoid to much problems with journal editors and referees addicted to

I have written a function called mcsamp() that is a wrapper that runs mcmcsamp() and automatically monitors convergence and structures the inferences into vectors and arrays as appropriate. But I have run into a very little problem, which is that mcmcsamp() shortens the variable names. For example: > set.seed (1) > group <- rep (1:5,10) > a <- rnorm (5,-3,3) > y <-

Hello, I am a chemical student and I make use of 'lme/lmer function' to handle experiments in split-plot structures. I know about the mcmcsamp and I think that it's very promissory. I would like knowing "the concept behind" of the mcmcsamp function. I do not want the C code of the MCMCSAMP function. I would like to get the "pseudo-algorithm" to understanding that

Hi All, I'm trying to add a 'rug' representation of my data to a plot created with densityplot(). While I can do this in the simple case, I can't do it properly when I include the "groups" argument. I have an example below. I am running a reasonably new version of R. print(sessionInfo()) R version 2.12.0 Patched (2010-11-07 r53537) Platform: i686-pc-linux-gnu

Hi Dear R users, For a pair plotting, usaully we use par( ) function. Apparently it does not work anywhere. I want to have 3 plots in a single figure, like this: par(mfrow=c(3,1)) densityplot( a) densityplot(b) densityplot(c) But it does not work. How is it possible to have such a figure with densityplot( ) in a single figure? So many thanks for any help. Amir Safari

Hi, I have the following script: ---- t.R --- grafica <- function() { v <- read.csv('preprocessed/komolongma.ece.uprm.edu.active',sep=',') x <- as.ts(v$active) bitmap(file="output.png") densityplot(~x,col='blue',main='Density Plot') dev.off() } grafica() ---- t.R --- When I "sourced" it from R prompt, it quietly runs.

Hi I would like to superimpose group-specific densityplots on top of an overall histogram using panel.histogram and panel.densityplot. Furthermore, I would like to automatically adjust the range of the y-axis to take into account the ranges of both histogram and densityplot. This last part is where I have a problem. I believe using the prepanel argument of histogram is typically the way to go,

Hello, I'm doing some graphics for a paper and a need customize such with filled region above the density curve. My attempts I get something very near what I need, but I don't solve the problem of use semitransparent filled. Below a minimal reproducible code. Someone has any idea? require(lattice) # toy data... dt <- expand.grid(A=1:2, B=1:3, y=1:50) dt$y <- rnorm(nrow(dt), dt$B,

Hi all, I'm trying to get a vertical line at a specific point in a densityplot. abline seems to be what's required, but it doesn't align itself to the scale used in the plot. example: library(lattice) x<-rnorm(100) plot.new() densityplot(x) abline(v=0) ----- The line seems to use some other coordinate system. What kind of call do I use to make abline use the graph's

I've been trying to keep track with lmer, and now I have a couple of questions with the latest version of Matrix (0.995-4). I fit 2 very similar models, and the results are severely rounded in one case and rounded not at all in the other. > y <- 1:10 > group <- rep (c(1,2), c(5,5)) > M1 <- lmer (y ~ 1 + (1 | group)) > coef(M1) $group (Intercept) 1 3.1 2

Just out of curiosity, why might this be occuring: > class(x6) [1] "mcmc" > crosscorr.plot(x6) NULL # Replicable code example(lmer) x6 <- mcmcsamp(fm1, n=1000) crosscorr.plot(x6)

Hi, i have a script how i launch lattice to make a densityplot. in the script: jpeg(file="XXX.jpg") densityplot(~f_diametro+m_diametro+n_diametro, plot.points="rug", auto.key=T) dev.off() does'nt work and in R i dont have any output but if i launch by R row by row, runs correctly..... any idea?

I am trying to plot multiple densityplots on each panel, and using panel.groups to do some additional plotting (not included in the example) as in this example. library(lattice) thedata <- data.frame(x=rnorm(1200),class=rep(1:3,each=200,times=2), group=rep(1:2,each=100,times=6)) densityplot(~x | class,groups=group, xlab='x', panel.groups =

Dear R users, I have now tried out several options of obtaining p-values for (quasi)poisson lmer models, including Markov-chain Monte Carlo sampling and single-term deletions with subsequent chi-square tests (although I am aware that the latter may be problematic). However, I encountered several problems that can be classified as (1) the quasipoisson lmer model does not give p-values when

Is there any function like par(new=T) for lattice. I want to plot a histogram in percentages on the right hand side and also superimpose the densityplot with its density scale on the lhs. so far I am only able to do this histogram( temp[,2]~ temp[,1],nint=100,type="desnity", xlab = "Population Size", panel = function(x, ...) {

I'm trying to plot a series of densities using/comparing differing weights. I see the reference to weights and subscripts, but I don't understand how to implement that. My data are of the form: I, J, Actual, Distance, Subset, Weight1, Weight2, ... I'm trying to see the effect of the distance distribution (Actual by Distance) compared to the various weighted distributions

densityplot(~x+y) does what I expect it to do if x and y have equal length. I know how to get what I want if x and y have different lengths. But what is this actually doing if x and y have different lengths? The relevant example is x=rnorm(10) y=rnorm(50,1) densityplot(~x+y)

Hi, I have downloaded a R script from http://www.wessa.net/rwasp_edauni.wasp#output. This script produces a densityplot graphic, amongst others, when is executed from the web page. However, when I run it in my machine the *densityplot* function produces any output, I mean a blank graphic. But, it's interesting if I run the following lines in the R interactive console: > y <-