similar to: plot.mids / Error in plot.new() : figure margins too large

When using a gtkDrawingArea as a Cairo device I very often encounter the error: "figure margins too large" Even for the below "getting started" example from http://www.ggobi.org/rgtk2/ this is the case. > win = gtkWindow() > da = gtkDrawingArea() > win$add(da) > asCairoDevice(da) [1] TRUE > plot(1:10) Fehler in plot.new() : Grafikr?nder zu gro? > Also

Dear all I try to print 9 plots on a page, arranged as the code shows below. nf <- layout(matrix(c(1,0,2,0,0,3,0,4,0,5,0,6,0,0,0,0,7,0,8,9), 10,2)) layout.show(nf) but when I try to plot, an error message Fehler in plot.new() : Grafikr?nder zu gro? appears to verify p.e. with plot(runif(10:1)) i tried with plot(runif(10:1), ann=F) to produce more space, but neither. The second

I am using -mice- for multiple imputation and would like to use the gelman diagnostic in -coda- to assess the convergence of my imputations. However, gelman.diag requires an mcmc list as input. van Buuren and Groothuis-Oudshoorn (2011) recommend running mice step-by-step to assess convergence (e.g. imp2 <- mice.mids(imp1, maxit = 3, print = FALSE) ) but this creates mids objects. How can I

Dear list members, how can multiple imputation realized for anova tables in R? Concretely, how to combine F-values and R^2, R^2_adjusted from multiple imputations in R? Of course, the point estimates can be averaged, but how to get standarderrors for F-values/R^2 etc. in R? For linear models, lm.mids() works well, but according to Rubins rules, standard errors have to be used together with

Hello I would like to pass a model formula as an argument to the with.mids function from the mice package. The with.mids functon fits models to multiply imputed data sets. Here's a simple example library(mice) #Create multiple imputations on the nhanes data contained in the mice package. imp <- mice(nahnes) #Fitting a linear model with each imputed data set the regular way works

Dear alltogether, I am searching for a way to determine "effect size" in multi-level models by using lme(). Coming from Psychology, for ordinary OLS there are measures (for meta-analysis, etc.) like CohensD <- (mean_EG - mean_CG) / SD_pooled or (p)eta^2 <- SS_effect / (SS_effect + SS_error) I do not intend to lead a discussion of the usefulness of such measures as long as

R-helpers: I have a dataset that has 168 subjects and 12 variables. Some of the variables have missing data and I want to use the multiple imputation capabilities of the "mice" package to address the missing data. Given that mice only supports linear models and generalized linear models (via the lm.mids and glm.mids functions) and that I need to fit Cox models, I followed the previous

With lme4, use of mcmcsamp can be insightful. (Douglas Bates drew my attention to this function in a private exchange of emails.) The distributions of random effects are simulated on a log scale, where the distributions are much closer to symmetry than on the scale of the random effects themselves. As far as I can see, this is a straightforward use of MCMC to estimate model parameters; it is not

Hi all, does anyone know how to automatically update starting values in R? I' m fitting multiple nonlinear models and would like to know how I can update starting values without having to type them in. thank all --- On Fri, 12/26/08, r-help-request@r-project.org <r-help-request@r-project.org> wrote: From: r-help-request@r-project.org <r-help-request@r-project.org> Subject:

Dear R-users, Version V2.0 of the package mice is now available on CRAN for Windows, Linux and Apple users. Multivariate Imputation by Chained Equations (MICE) is the name of software for imputing incomplete multivariate data by Fully Conditional Specifcation (FCS). MICE V1.0 appeared in the year 2000 as an S-PLUS library, and in 2001 as an R package. MICE V1.0 introduced predictor selection,

Dear R-users, Version V2.0 of the package mice is now available on CRAN for Windows, Linux and Apple users. Multivariate Imputation by Chained Equations (MICE) is the name of software for imputing incomplete multivariate data by Fully Conditional Specifcation (FCS). MICE V1.0 appeared in the year 2000 as an S-PLUS library, and in 2001 as an R package. MICE V1.0 introduced predictor selection,

Dear altogether, I tried local regression with the following data. These data are a part of a bigger dataset for which loess is no problem. However, the plot shows extreme values and by looking into the fits, it reveals very extreme values (up to 20000 !) although the original data are > summary(cbind(x,y)) x y Min. :1.800 Min. :2.000 1st Qu.:2.550

Dear alltogether, I experience very strange behavior of imputation of NA's with the NORM library. I use R 2.2.0, win32. The code is below and the same dataset was also tried with MICE and aregImpute() from HMISC _without_ any problem. The problem is as follows: (1) using the whole dataset results in very strange imputations - values far beyond the maximum of the respective column, >

Dear list members, the following hlm was constructed: hlm <- groupedData(laut ~ design | grpzugeh, data = imp.not.I) the grouped data object is located at and can be downloaded: www.anicca-vijja.de/lg/hlm_example.Rdata The following works: library(nlme) summary( fitlme <- lme(hlm) ) with output: ... AIC BIC logLik 425.3768 465.6087 -197.6884 Random effects:

Dear R-list members, I have a problem with translating a mathematica script into R. The whole script is at the end of the email (with initial values for easy reproduction) and can be pasted directly into R. The problematic part (which is included below of course) is <--- Original Mathematica ---> (* p_svbar *) UiA = Ni (Dsi - 2Di A + A^2)/2; UiiA = Nii (Dsii - 2Dii A + A^2)/2; psvbar =

# BUG 1: returns empty vector if no NAs present > nona <- 1:10 > na.omit(nona) numeric(0) na.omit.default() has two lines where object[-omit] is used, where omit represents positions of NAs and thus without NAs omit = numeric(0) and since -numeric(0) = numeric(0) object[-omit] = numeric(0) It looks like an earlier version of na.omit.default had 'omit' defined as

Dear list, I'd like to use multiple imputations to try and save a somewhat badly mangled dataset (lousy data collection, worse than lousy monitoring, you know that drill... especially when I am consulted for the first time about one year *after* data collection). My dataset has 231 observations of 53 variables, of which only a very few has no missing data. Most variables have 5-10% of

Dear altogether, I want to blank the lower (or upper) part of a correlation matrix as it is done by dist() example: ( d <- cor(matrix(runif(12),nrow=4)) ) If I do the following d[lower.tri(d)] <- "" of course everything is changed to character - that's not what should be. Additionally, it does not work to assign "0" or anything else. The same is true for

I discovered a bug in hist(). Try the following: x<-c(-5,-4,-4,-4,-3,-3,-3,-3,-2,-2,0,0,0,0,1,1,1,3,3,5,6) # Note that sum(x)<0: sum(x) # [1] -13 hist(x) # looks ok hist(x,freq=F) # negative bars !! # and finally this gives not 1: sum(hist(x,plot=F)$rel.freqs) # [1] -0.8076923 The reason is, that "sum(x)" is used instead of "length(x)" in the following line near

Dear All, This is my first post to this mailing list (and yes, I did read http://www.r-project.org/posting-guide.html ) so please forgive any faux pas. I'm trying to make a visualization that looks like this http://www.gradient-da.com/img/temperature%20surface%20plot%20470x406.JPG(found through google). The idea is to have a 3D surface plot overlapping a 2d representation of a surface. I can