similar to: Is the Intercept Term always in First Position?

Dear All, Suppose I want to write a method for the generic function confint(): > args(confint) function (object, parm, level = 0.95, ...) So, it looks like the second and third argument have been predefined in the generic function. Suppose one or several of the predefined arguments don't apply or fit (in some sense) with the design of the rest of the package. What should one do? I see

Philipp Benner reported a Debian bug report against r-cran-rpart aka rpart. In short, the issue has to do with how rpart evaluates a formula and supporting arguments, in particular 'weights'. A simple contrived example is ----------------------------------------------------------------------------- library(rpart) ## using data from help(rpart), set up simple example myformula <-

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

Hi, I am passing a data frame and field name to a function. I've figured out how I can create the formula based on the passed in field name, but I'm struggling to create a vector based in that field. for example if I hard code with the actual field name Y = df$Target, everything works fine. but if I use the passed in parameter name, it doesn't give me what I want, Y =

Hi everyone, Can someone please help me in these questions?: 1)if I use crossvalidation with svm, do I have to use this equation to calculate RMSE?: mymodel <- svm(myformula,data=mydata,cross=10) sqrt(mean(mymodel$MSE)) But if I don’t use crossvalidation, I have to use the following to calculate RMSE: mymodel <- svm(myformula,data=mydata) mytest

Hi, I am passing a data frame and field name to a function. I've figured out how I can create the formula based on the passed in field name, but I'm struggling to create a vector based in that field. for example if I hard code with the actual field name Y = df$Target, everything works fine. but if I use the passed in parameter name, it doesn't give me what I want, Y = df$mytarget

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.

Hi, Could you please tell me how I correct the following error message? “Error in object$tables[[v]] : subscript out of bounds” This is the code: library(e1071) data(iris) attach(iris) class_label <- names(iris)[1] myformula <- formula(paste(class_label,"~ .")) mymodel<-naiveBayes(myformula, iris,cross=3) predict(mymodel,iris) ##Error in object$tables[[v]] :

In S3 if you are passing an evaluated formula to another function that will store it and use it in printing (such as a model-fitting function), you usually want to unclass the formula. Otherwise, the formula that gets stored looks very ugly when printed. A trick to do the unclassing is to pass form = c( myFormula ) In R this has the effect of making form a list rather than a call. It seems

Hello! I am trying to create a formula object using as.formula. But it's not working: examplephraze<-"for.my.example" myformula<-as.formula(paste(examplephraze,"~group, sum, data=mydata",sep="")) What's the problem? Thanks a lot! -- Dimitri Liakhovitski Ninah Consulting www.ninah.com

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 am wanting to create a model matrix and keep the NAs. stratmat <- model.matrix(myformula,mydata) Is there any way to do this? model.matrix doesn't have na.action as a parameter. Elsewhere I have made use of na.keep <- function(x){x}. Many thanks, Rachel Cunliffe -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

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

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

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 <-