similar to: Combing forest plots

Hello All, I am having a problem creating text for my forest plot using the "forestplot" function in the rmeta package. I think my problem is that I have too many columns of text, so I was wondering if there is a way to shrink my text or to change the default setting. Here is an example: #### forest_mean<- as.numeric(c(NA, 1.00, 2.00, NA, 0.75, 1.10)) forest_lower<-

Dear all, I would like to draw three forest plots to represent results at years 1, 2 and 3. I have the data as point estimates and 95% confidence intervals. Using the following code I can get three basic forest plots - the first which has the table of results. I have to plot each separately as the usual par(mfrow=c(3,1)) does not work with the function forestplot within rmeta. I can easily put

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:

I know there is a function forestplot from rmeta package and also the plot.meta from the meta package and maybe others, but they are rather complicated with extra plot parameters that I do not need and also they process only objects created with other package functions. But I wonder if anyone has a much simpler function using the basic plot to make a forestplot with only a median (or mean) and

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 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

Hello, I have a general formatting question. I have two columns of data: ColA <- c("m", "m", "m", "m") ColB<- c("d","d","d","d") And I would like to reorder them into a new column that looks like this: ColC<-

Dear Sir or Madam, I am trying to plot forest plot. I extracted odds ratio and their corresponding 95% confidence interval from papers, then I calculated the log(OR) and standard error using the following command OR<-metagen(logOR,selogOR,sm="OR") forest(OR,comb.fixed=TRUE,comb.random=TRUE,digits=2) However, it does not produce a forest plot. Can someone kindly help? Thank

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,

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 <-

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

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

Hello All, I need to create a three-way interaction plot and I was wondering if anyone could recommend a package I could use to design these plots. Thank you! ________________________________ PHRI DISCLAIMER This information is directed in confidence solely to the person named above and may not otherwise be distributed, copied or disclosed. Therefore, this information should be considered

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

Greetings I have been trying to use metaplot to create a forest plot of my meta-analysis results. My data is in the form of ORs, so I've been inputting the ORs as the point estimate (mn) and the SEs of the logORs as the SE (se). However, whilst the point estimates appear to be displayed properly, many of the confidence intervals go below zero (not possible for an OR, as far as I know). I

Hi All, I have a question regarding contingency tables. I would like to calculate the mean and standard deviation of a continuous variable from my own dataset based on the percentages of a contingency table I obtained from a scientific article. dataset<- data.frame(cbind(case=rep(0:1,5), x1=c(1:10), x2=c(0:9))) contingency_table<- matrix(c(100, 75, 65, 85, 90, 87), nrow=3, ncol=3) In

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

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

Hi, It is known that the area of square plotted by forestplot is proportional to the studies' weights. But since there is not weight parameter in forestplot function, how does this function proportion the area of each square based on the related study's weight? Look forward to your reply, Carol [[alternative HTML version deleted]]