similar to: effect sizes in lme/ multi-level models

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 altogether, is it possible to integrate "weights" arguments within lmer to incorporate statements to handle heteroscedasticity as it is possible with lme? I searched the R-archive but found nothing, insofer I assume it is not possible, but as lmer is under heavy develpoment, maybe something changed or is solved differently. Thus my question: While encountering heavy

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, >

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 alltogether, how can a delay like possible with par(ask=TRUE) be attained while using trellis-plots within a loop or something like that? the following draws each plot without waiting for a signal (mouse-klick), so par() does not work for that: library(nlme) for(i in 1:3) { fitlme <- lme(Orthodont) par(ask=TRUE) # does not work with trellis.... print(

Dear alltogether, two lme's, the data are available at: http://www.anicca-vijja.de/lg/hlm3_nachw.Rdata explanations of the data: nachw = post hox knowledge tests over 6 measure time points (= equally spaced) zeitn = time points (n = 6) subgr = small learning groups (n = 28) gru = 4 different groups = treatment factor levels: time (=zeitn) (n=6) within subject (n=4) within smallgroups

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

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

The vp3 / theora decoder in FFmpeg project received a major speedup: http://multimedia.cx/eggs/profiling-and-optimizing-theora/#comment-150751 http://git.ffmpeg.org/?p=ffmpeg;a=commit;h=6e8061980c9a18a0eb9802f5dc5becc349066680 http://git.ffmpeg.org/?p=ffmpeg;a=commit;h=8476744ba50d26a3e188def192cd9086b0f781d7 http://git.ffmpeg.org/?p=ffmpeg;a=commit;h=a51faef9b61bd80499a28322802ef4b22be44d43

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 =

Hello R-Users, I would like to check the convergence of my imputations. However, when I use the function the plot.mids(), I always obtain the following error message Error in plot.new() : figure margins too large I read the same question in thread from November 2005 (see below). I actually have the same problem. Is it now possible to plot subsets of mids.objects. If yes, how? My

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 alltogether, how is it possible to assign the colnames of a data.frame to a function called by apply, e.g. for labeling a plot? Example: I want to plot several qqnorm-plots side by side and there should be a maintitle for each qqnorm-plot which is identical to the respective colname. I checked, but the column which is processed by the function called by apply does not contain a colname

Dear R-list, I try to transform a mathematica script to R. #######relevant part of the Mathematica script (* p_sv *) dd = NN (DsD - DD^2); lownum = NN (L-DD)^2; upnum = NN (H-DD)^2; low = lownum/(2s^2); up = upnum/(2s^2); psv = NIntegrate[1/(s^NN) Exp[-dd/(2s^2)] (Gamma[1/2,0,up] + Gamma[1/2,0,low]),{s,sL,sH}, MinRecursion->3]; PSV = psv/Sqrt[2NN]; Print["------------- Results

Dear alltogether, I want to use CGIwithR in conjunction with R2HTML. A small example called 'test.R': ##### #! /usr/bin/R invisible(capture.output(library(R2HTML))) HTML(summary(as.numeric(scanText(formData$numbers))), file=stdout()) ##### The script gets its input via 'CGIwithR.cgi' and contains the variable "numbers." The 'HTML' output (-> summary() in