similar to: Writing methods for existing generic function

Dear All, I have a question about formulas and model.matrix(). If one specifies a model via a formula, the corresponding design matrix can be obtained with the model.matrix() function. For example: x1 <- c(1,4,2,3,5) x2 <- c(1,1,2,2,2) myformula <- ~ x1 + factor(x2) model.matrix(myformula) My question is: If an intercept term is in the model (like in the example above), is it always

Hi, I have been looking for an R package which allowed to do meta-analysis (both pairwise and network/mixed-treatment) at arm-level rather than at trial-level, the latter being the common way in which meta-analysis is done. By arm-level meta-analysis I mean one that accounts for data provided at the level of the individual arms of each trial and that does not simply derive the difference between

Dear Wolfgang Viechtbauer and R users, I have few questions regarding the development of the package 'metafor. As you suggested , I post to the R-help mailing list. I read you're planning an extension of this method to the multivariate case. I think it would be a useful tool. I'm currently performing some analyses with R on multiple outcomes, using the Stata command mvmeta to get

This is a question of clarification. IN 2009 Higgins, Thompson and Spiegelhalter (J R Statist Soc A 172:137-159) gave WinBUGs code to get credible intervals from random effects meta analysis for the prediction interval of a new study. It appears that the predict.rma function creates approximate credible intervals (pending a function revision by the author) for that purpose. Is my assumption

Hi All, I want to produce a similar "Forest Plot" as it is on the following link, but my data would be having only two columns (one for "Estimate" and other for "Std. Dev"). Can anyone suggest some function() {Package} which can take such file as an input and give following forest plot:

Hi everyone, I'm using the metafor package to meta-analyze a set of proportions. This is working really well for the raw proportions, but is there a way to back-transform the arcsine transformed proportions in the rma or forest functions with the atransf option? The estimates and CIs for the transformed proportions need to be back-transformed to be the sin of the estimate squared.

influence.measures gives several measures of influence for each observation (Cook's Distance, etc) and actually flags observations that it determines are influential by any of the measures. Looks good! But how does it discriminate between the influential and non- influential observations by each of the measures? Like does it do a Bonferroni-corrected t on the residuals identified by

Dear R-community, I'm currently trying to assemble a forest plot using the "forest" function from package "metaphor". Works well. Even the regular "main"-argument works for adding a title to the graph. However, I would like to add one top row which explains the nature of the columns. Very much like the usual header in spreadsheet programs. For example:

Dear R-help, I am interested in using structural equation modeling. Just getting started with it, but I'm looking for suggestions for packages. As an aside, what's the best way for looking for packages at CRAN? -- Robert Terwilliger Biomedical Physicist Laboratory of Neurocognitive Development Western Psychiatric Institute and Clinic University of Pittsburgh Medical Center Loeffler

I have a data set with 50 different x values and 5 values for the sampling variance; each of the 5 sampling variances corresponds to 10 particular x values. I am trying to fit a mixed effect linear model and I'm not sure about the syntax for specifying the fixed variance structure. In Pinheiro's book my situation appears to be similar to the example used for varIdent, where there is a

Hello: I am trying to understand the method 'hatvalues(...)', which returns something similar to the diagonals of the plain vanilla hat matrix [X(X'X)^(-1)X'], but not quite.  A Fortran programmer I am not, but tracing through the code it looks like perhaps some sort of correction based on the notion of 'leave-one-out' variance is being applied. Whatever the

I was not sure who I should contact about this, so I am posting this here. There are a few broken links on the R website. 1) http://www.r-project.org/search.html -> link to the Nabble R Forum. I belive the correct/new URL should be: http://r.789695.n4.nabble.com/ 2) http://www.r-project.org/other-docs.html -> link to "Auswertung ?kologischer Daten". Not sure if there is a new

Dear all, I'm currently applying a mixed model approach to meta analysis using the package metafor. I use the "model.matrix()" function to create dummy variables. The option btt gives me the combined test for the dummies. Problem is, I don't know which indices I have to use, and can't really figure it out from the help file and the examples. I use following code : X <-

Dear R community, I'm using the ,metafor'-package by Wolfgang Viechtbauer (Version: 0.5-5) to calculate random-effects meta-analyses using Correlations and Sample Sizes as the raw data. (By the way: Really a nice piece of work, Wolfgang! Thanks heaps.) I specified the "rma.uni' function so that it looks like this: MAergebnis<-rma.uni(ri=PosOutc, ni=N,

Hi, I am working with the package nlme, and I tried creating a new correlation class (which, according to the help pages, is possible if you write a few new method functions). Anyways, I think I am 99% of the way there, but I have a recurring problem with R crashing on seemingly innocuous statements (I have set debug() on nearly every function, so I can see where it is failing). For instance,

Dear All, I have been checking the metafor package against R-devel. R CMD check --as-cran metafor yields one note: FOSS licence with BuildVignettes: false Yes, I have 'BuildVignettes: FALSE' in my DESCRIPTION file. I see at http://developer.r-project.org/blosxom.cgi/R-devel/NEWS: Tue, 25 Jun 2013 CHANGES IN R-devel UTILITIES 'R CMD check --as-cran' warns about a false

Hello, I have written a simple Metropolis-Hastings MCMC algorithm for a binomial parameter: MHastings = function(n,p0,d){ theta = c() theta[1] = p0 t =1 while(t<=n){ phi = log(theta[t]/(1-theta[t])) phisim = phi + rnorm(1,0,d) thetasim = exp(phisim)/(1+exp(phisim)) r = (thetasim)^4*(1-thetasim)^8/(theta[t]^4*(1-theta[t])^8) if(runif(1,0,1)<r){ theta[t+1] = thetasim }

Hi All, I am compiling R-devel on Linux (Ubuntu 16.04.2) and that works just fine, but I am missing some of the expected features, as shown here: https://cran.r-project.org/doc/manuals/r-devel/R-admin.html#Linux In particular, after configure (using all defaults), I see: R is now configured for x86_64-pc-linux-gnu [...] Interfaces supported: X11 External libraries:

Dear All, I am working on a function that has an optional data argument, just like lm(). If the user sets the data argument equal to some dataframe, then the function should look inside the dataframe for the variables given to other arguments, otherwise the variables should be accessible from what I believe is called the parent frame from which the function was called. I looked at the source code

Hi, I have been using the function lme() of the package nlme to model grouped data that is auto-correlated in time and in space (the data was collected on different days via a moving monitor). I am aware that I can use the correlation classes corCAR1 and corExp (among other options) to model the temporal and spatial components of the auto-correlation. However, as far as I can tell, I can only