search for: zvals

...uot;) to work in llines(). Try, e.g., the following scaled-down example: x <- factor(c("a", "b", "a", "b")) y <- c(1,2,1,2) z <- factor(c("A", "A", "B", "B")) symbols <- 1:2 lines <- 1:2 colors <- 1:2 zvals <- levels(z) xyplot(y~x|z, panel = function(x, y, subscripts, ...) { for (i in 1:length(zvals)) { sub <- z[subscripts] == zvals[i] llines(x[sub], y[sub], lwd = 2, type = "b", col = colors[i], pch = symbols[i], lty = lines[i]) } }) Only...

Adapt to the API changes in the newer versions of the Zend framework, in particular regarding: - strings handling - resources handling (used for the guestfs_h pointer) - iterating in hash maps - data types for function arguments Introduce helper macros to reduce greatly the amount of #if's all around the generated C code. --- generator/php.ml | 96

OptString maps to a "s!" argument, which makes zend_parse_parameters not touch the variables (char* and length) when NULL is passed as parameter. Hence, set both to NULL/0, and check for non-NULL char* variable before checking its length. --- generator/php.ml | 15 +++++++++++++-- 1 file changed, 13 insertions(+), 2 deletions(-) diff --git a/generator/php.ml b/generator/php.ml index

Hello, I am a new R user. More precisely, I am working with JRI (on a Eclipse Java6 project under Ubuntu9). I have difficulties to access some R packages (For example: package "stats", object "Normal", function "pnorm"). I have tried many solutions to set the right path but nothing have succeed: For example:

is there no package/function in R to calculate the conditional power or the bayesian predictive power for trials with binary endpoints? Thanks -- View this message in context: http://www.nabble.com/Conditional-power%2C-predictive-power-tf3603396.html#a10066991 Sent from the R help mailing list archive at Nabble.com.

Hi all. I have problem compiling xapian php bindings. I am using debian sarge. I have installed php4-dev before compilation. I am getting the following errors: boomer:~/xapian/xapian-bindings-0.9.0# make make all-recursive make[1]: Entering directory `/root/xapian/xapian-bindings-0.9.0' Making all in php4 make[2]: Entering directory `/root/xapian/xapian-bindings-0.9.0/php4' make

I'm puzzled by the behaviour of factors in rma models, see example and comments below. I'm sure there's a simple explanation but can't see it... Thanks for any input John Hodgson ------------------------------------- code/selected output ----------------- library(metafor) ## Set up data (from Lenters et al A Meta-analysis of Asbestos and Lung Cancer... ##

Hi, I'm using metafor to perform a mixed-effects meta-analysis. I'd like to test whether the effect is different for animals and plants/whether "group" (animal/plant) influences the effect size, but am having trouble interpreting the results I get. I've read previous posts about QM in metafor, but I'm still a bit confused. I've dummy-coded the factors:

I am using the coxph, survfit and summary.survfit functions to calculate an estimate of predicted survival with confidence interval for future patients based on the survival distribution of an existing cohort of subjects. I am trying to understand the calculation and interpretation of the std.err and confidence intervals printed by the summary.survfit function. Using the default confidence

...trast with all I read on this issue, including R mailing lists (e.g. http://finzi.psych.upenn.edu/R/Rhelp02/archive/30588.html), e.g. if someone claims those lines would not matter and go away when printing. In my case they don't. BTW, my image() statement looks like this: image(rfc, y, -t(zVals), zlim=c(-noCols-0.5,0.5), col = heat.colors(noCols), axes=F, add=T) where z is a 127 x 3 matrix. Since I need high resolution variants for final publication I need to know whether it is possible to force image() to give up drawing these borders (I tried already several low level par parameter...

Dear R-Helpers, I want to compare the results of outputs from glmmPQL and lmer analyses. I could do this if I could extract the coefficients and standard errors from the summaries of the lmer models. This is easy to do for the glmmPQL summaries, using > glmm.fit <- try(glmmPQL(score ~ x*type, random = ~ 1 | subject, data = df, family = binomial), TRUE) > summary(glmmPQL.fit)$tTable

Hello metafor users, I'm using metafor to perform a single-effect summary estimate of the raw proportion of patients experiencing a post-operative complication, and I'm interested in seeing if this proportion differs between the three most commonly used surgical techniques. The software is working as expected, but I would like to double check on the interpretation of my mixed-effect model

Dear Metafor users, I'm conducting a metaanalysis of prevalence of a particular behaviour based on someone elses' code. I've been labouring under the impression that this: summary(rma.1<-rma(yi,vi,mods=cbind(approxmeanage,interviewmethodcode),data=mal,method="DL",knha=F,weighted=F,intercept=T)) is doing the multivariate analysis that i want, but have read that

Hello, I am having a bit of trouble with scatterplot3d(). I was able to plot a 3d cloud of points using the following code: >my.3dplot<-scatterplot3d(my.coords, pch=19, zlim=c(0,1), scale.y=0.5, angle=30, box=FALSE) where my.coords is a data frame that contains x, y, and z coordinates for grid points whose elevation we sampled. The problem occurs when I try to add points using points3d.

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I had found the author's (Wolfgang Viechtbauer) earlier meta-analytic code in R, MiMa, useful. so I have been exploring metafor using an example dataset from MiMa. metafor provides a lot more. However, MiMa provided parameter estimates, standard errors, z values, etc. for individual moderators in the meta-analysis, but I don't see how to obtain these from metafor. Have you any help

Hi I have tryed to do the meta-regression in metafor package, but I would like to get the standardized coefficients for each variable, however in command:   Ø  res<-rma.uni (yi, vi, method="REML", mods=~cota+DL+uso+gadiente+idade, data= turbidez)   I just have the coefficients no standardized (estimate) of the multiple regression. What I need to do? Thanks Fernanda Melo

Dear colleagues, I'm conducting a meta-analysis of studies evaluating adherence of HIV-positive drug users into AIDS treatment, therefore I'm looking for some advice and syntax suggestion for running the meta-regression using proportions, not the usual OR/RR frequently used on RCT studies. Have already searched already several handbooks, R-manuals, mailing lists, professors, but... not

Hello, I am performing a meta-analysis using the metafor package. My data are proportions and I used the Freeman Tukey double arcine (FT) transformation to fit the random effects model. Now I want to create a forest plot with my estimates backtransformed to the original scale of proportions. Can this be done? Regards, Patricia

https://bugzilla.redhat.com/show_bug.cgi?id=847880 https://bugzilla.redhat.com/show_bug.cgi?id=847881 This patch series adds various optional arguments to the tar-in and tar-out commands. Firstly (1/7) an optional "compress" flag is added to select compression. This makes the calls tgz-in/tgz-out/txz-in/txz-out deprecated, and expands the range of compression types available.