similar to: Overlaying density plot on forest plot

Dear R-experts, The forest.rma() function from the metafor package creates nice forest plots for presenting the results of a meta-analysis. These plots can be annotated for e.g. giving names to the columns. E.g. as in the documentation of the package: data(dat.bcg) ### meta-analysis of the log relative risks using a random-effects model res <- rma(ai=tpos, bi=tneg, ci=cpos, di=cneg,

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:

Hi all, rmvnorm()can be used to generate the random numbers from a multivariate normal distribution with specified means and covariance matrix, but i want to specify the correlation matrix instead of covariance matrix for the multivariate normal distribution. Does anybody know how to generate the random numbers from a multivariate normal distribution with specified correlation matrix? What about

I get the following error when I try reading in a table. How are 1.1, 1.2, 1.3 duplicate row names? Thx. > table <- read.table('latestWithNumber.txt', header=T) Error in "row.names<-.data.frame"(`*tmp*`, value = c("1.1", "1.2", "1.3", : duplicate 'row.names' are not allowed Yongchuan

Hi all, I have a problem to estimate Std. Error and t-value by ?polr? in library Mass. They result from the summary of a polr object. I can obtain them working in the R environment with the following statements: temp <- polr(formula = formula1, data = data1) coeff <- summary(temp), but when the above statements are enclosed in a function, summary reports the following error:

If I am buying a PC where the most compute intensive task will be running R and I do not have unlimited resources what trade-offs should I make? Specifically should I go for 1 - more memory, or 2 - faster processor, or 3 - something else? If it makes a difference I shall be running Windows on it and I am thinking about getting a portable which I understand makes upgrading more difficult. Extra

Hi, Anybody know what are the common Standard statistical methods used for the analysis of a dataset,and anybody know which of these methods give similar results Ram [[alternative HTML version deleted]]

I have been successfully been using R on my EEE PC for some while now. Although it uses Xandros I have been able to install various pieces of software by pretending that it was Debian etch. Johannes was kind enough to continue providing binaries on CRAN for a while but I think I am the only person still living in the past as CRAN etch is currently stuck at 2.11.0 and I cannot rely on him to

Hello! I have some data stored into 2 separate csv file. 1 file (called A.csv) (12 results named Group1, Group2, Group3, etc...) odds ratios, 2 file (called B.csv) 12 corresponded errors. How to import that data into R and make forest plot like I saw inside help file Rmeta and meta with included different font colors and names trough X and Y axis. I know for meta libb ... out <-

In fact my first vignette full stop. I am intending to use Sweave. I have read the Sweave documentation and section 1.4 of the extensions manual and apart from (a) do not use split = TRUE (b) and include all the source components, there does not seem to be anything CRAN specific. 1 - Am I missing some other documentation? 2 - If I use a package from CTAN would that be considered within the

consider p as random effect with 5 levels, what is difference between these two models? > p5.random.p <- lmer(Y ~p+(1|p),data=p5,family=binomial,control=list(usePQL=FALSE,msV=1)) > p5.random.p1 <- lmer(Y ~1+(1|p),data=p5,family=binomial,control=list(usePQL=FALSE,msV=1)) in addtion, I try these two models, it seems they are same. what is the difference between these two model. Is

Dear users, Does anyone have any idea how to generate standardised beta coefficients for a ZINB model in R to compare which explanatory variables are having the greatest impact on the dependent variable? Thanks, Jeremy [[alternative HTML version deleted]]

Hi, I have a problem when I try to generate the Documentation pdf (from .rda files)in Spanish during the package creation. Could you tell me the way I can do it?. Thanks in advance. Regards. Eva [[alternative HTML version deleted]]

I am looking for an R package with which one can calculate an effect size for a set of contrasts given an omnibus chi-square test statistic (more than 1 degree of freedom). Is there such a package? Presumably, it would implement the procedure (or something like it) described by Roznow and Rosenthal 1996 Psychological Methods 1: 331-340. many thanks! Steven Orzack Fresh Pond Research Institute

Hi, I need to create the correlation table of a set of categorical data (sex, marital_status, car_type, etc.) for a given population. Basically what I'm looking for is a function like cor( ) working on factors (and, if possible, considering NAs as a level). To better express myself, the manual way should be: 1) to create a set of n-1 numeric dummies (whose assigned values are

Would like to know how much data can R process - number of rows and columns? [[alternative HTML version deleted]]

Hello, I once used the metawin software to perform a meta-analysis (see metawinsoft, Rosenberg et al.) and produced normal qqplot to test for a potential bias in the dataset. I now want to re-use the same dataset with the package metafor by W. Viechtbauer (great package btw). I run the qqnorm.rma.uni function. I use standardized effect sizes as in metawin. QQplot generated with metafor differs

Hello, I found in the forum two threads about point biserial correlation. One of them (1) mentioned "a point-biserial correlation is just a Pearson correlation where one of the variables is dichotomous. Thus, the command is just the normal cor function". The other (2) mentioned "Professor Fox's package polycor" as a way to calculate point biserial correlation?

Hi all, I am running Stepwise logistic regression and i have : 1- Multiple covatiates included in each model (No missing data) 2- Genotype data (SNPs) about 500,000 . I partitioned the data to multiple files (there are missing data) I run the step by including all the covariates and one SNP at each model. but i got this message : number of rows in use has changed: remove missing values? In

Greetings, I wish to conduct a meta-analysis for which the outcome is a continuous variable measured on the same individuals before and after an intervention. Hence, the comparison is not made between two groups, but within groups, at diffrent times. Each study reports the mean outcome and SD before the intervention and the mean outcome and SD after the intervention. While p-values for