similar to: Nine questions about methods and generics

Dear R users: In the example of meta-analysis (cochrane, package rmeta), I can not found the p-value of Test for overall effect, and some other indices (Z, I, weight and et al). How can I get the these indices listed? > library(rmeta) > data(cochrane) > cochrane name ev.trt n.trt ev.ctrl n.ctrl 1 Auckland 36 532 60 538 2 Block 1 69 5

Dear Michael Camann, My concern was after having a hetero-computing environment for many years, I found that I had to keep an old version of Microsoft Windows based applications to be able to interact with my constituents. This at times can become very expensive. I have had to limit my budget on the UNIX/Linux platform to compensate for Microsoft Windows based applications. With Open Office

Dear Michael Camann, My concern was after having a hetero-computing environment for many years, I found that I had to keep an old version of Microsoft Windows based applications to be able to interact with my constituents. This at times can become very expensive. I have had to limit my budget on the UNIX/Linux platform to compensate for Microsoft Windows based applications. With Open Office

Dear users; I am doing GLMs with the Gamma distribution, and I always get errors ("no valid set of coefficients: please supply starting values") or warnings ("NaNs produced in log(x)") when I use the links identity or inverse, but I don´t get them if I use the log link. For example: >

Dear all, I could not draw a forest plot for meta-analysis under ramdon models using the rmeta package. The rmeta has a default function for MH (fixed-effect) model. Has the rmeta package been updated for such a function? Or someone revised it and kept a private code? I would appreciate it if you could provide some information on this question. Thanks, Andrew This email is intended

Der useRs, I'm working on meta analysis using rmeta package. Using code below I plot the forest plot: library(rmeta) data (catheter) a<-meta.MH (n.trt, n.ctrl, col.trt, col.ctrl, data=catheter, names=Name, subset=c(13,6,5,3,7,12,4,11,1,8,10,2)) summary(a) # odds ratio values and confidence intervals metaplot(a$logOR, a$selogOR, nn=a$selogOR^-2,a$names, summn=a$logMH, sumse=a$selogMH,

No need to reply to this message! http://www.openoffice.org/ Open Office 1.0.1 (OO) I got a change to have my network setup as; UNIX/Linux and Microsoft Windows, a hetero-computing network platform. I found that Open Office 1.0.1 (OO), a UNIX/Linux/Microsoft Windows based application package, works well under MS Windows ME. OO offers a word processor, spreadsheet, presentation and a HTML

Hi, I have a problem with the metaplot-function in rmeta. After plotting a Forest Plot with "metaplot", no y-axis is shown for normal plots anymore. Does someone know, why this is and how I can solve it? Thank you in advance! Kind regards, Roman -- View this message in context: http://www.nabble.com/metaplot-in-rmeta%3A-y-axis-disappears-tp24917401p24917401.html Sent from the R

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 <-

Dear R users, I'm trying figuring out with forest plot and rmeta. Here's my code... library(rmeta) tabletext <- structure(c("", NA, "Sex", "[F]", "[M]", NA, "Age class", "(0,60]", "(60,80]", NA, "Karnofsky class", "[70;90]",

Dear R-list, I'd like to do a meta-analysis plot similar to install.packages('rmeta') require(rmeta) data(catheter) a <- meta.MH(n.trt, n.ctrl, col.trt, col.ctrl, data=catheter, names=Name, subset=c(13,6,5,3,7,12,4,11,1,8,10,2)) summary(a) plot(a) (see attached file) by using my own OR (Odds Ratio) and 95% Confidence Interval data set, which looks like

Hello, I'm using the rmeta package to perform a meta analysis using summary statistics rather than raw data, and would like to analyze the effects in three different subgroups of my data. Furthermore, I'd like to plot this on one forest plot, with corresponding summary weighted averages of the effects displayed beneath each subgroup. I am able to generate the subgroup analyses by simply

Chandler, Thank you for getting it up to ML top. I believe we have to move broader than that you just mentioned. The natural separation of the infrastructure into different parts can be across the following lines: - the parallel model of programming - these can be OpenMP, OpenACC, CilkPlus, OpenCL, StreamExecutor, CUDA, C++ parallel extensions, etc. - the offloading machinery to be used by any

Hello, I have a data with following format:      Predictors          n11     n12     n21     n22     Odds.Ratio    log.ratio    se.log.odds. 1 ProcOR respirato     2 ProcVaric vein      3 DiagCardiac anom   4 DiagAllergy        5  DiagOth skin dx    6    DiagGastritis          I want to plot odds ratio by command: forestplot in rmeta library, but I get the following error constantly.  Error in

OK - I've been doing some work on getting a forest plot or two together for a sub-group analaysis. Thats the crucial thing - because its a sub-group analysis rather than a meta-analsysis and all the forest (meta) and forestplot (rmeta) instructions assume you are doing a meta-analysis. I found rmeta didn't play nicely for me so I'm using the meta package with forest. Here's some

Dear R users, I am trying to create a forest plot with a table of text using the forestplot() function in the rmeta package. I have been trying to reduce the font size of the resulting table of text, but have not been successful. I have tried adding options like 'cex' or 'font' but none of them seem to work. Is there anything I could do? This is what I have so far:

Dear Ioannis Thank you very much for pointing me to meta-analysis. Although it may not solve my problem with the normalization, it gives me some other options to display the different correlation coefficients. One possibility is the use of Funnel plots, which are even available in library(rmeta). Another possibility is the use of forest-plots, as implemented in rmeta as metaplot. Sorrowly,

Please pardon an extremely naive question. I see related earlier posts, but no responses which answer my particular question. In general, I'm very confused about how to do variance decomposition with random and mixed effects. Pointers to good tutorials or texts would be greatly appreciated. To give a specific example, page 193 of V&R, 3d Edition, illustrates using raov assuming pure

All, I am using the forestplot function in rmeta. I was able to modify the x axis range by commenting out one line and feeding it two new parameters (I wanted to set zero as the axis start point). #xrange <- c(max(min(lower, na.rm = TRUE), clip[1]), min(max(upper, na.rm = TRUE), clip[2])) #new line xrange <- c(xlow,xhigh) Now I am trying to modify the text font size for

Hi All, I have been able to modify the x-axis to start at zero by adding xlow and xhigh parameters; that was pretty simple. I have been unable to find the location of the code that would turn off the information weighting of the box size (I have smaller randomized trials getting less weight than a much larger non-randomized trial). The function is forestplot() from rmeta. Thanks for any