similar to: ordering of additional columns in forest plot in meta package

I'm sure there's a simple way to do this, but I can only think of complicated ones. I have a number of character vectors that look something like this: "12 78 23 9 76 43 2 15 41 81 92 5(92 12) (81 78 5 76 9 41) (23 2 15 43)" I wish to get it into a list of numerical vectors like this: $Group [1] 12 78 23 9 76 43 2 15 41 81 92 5 $Subgroup1 [1] 92 12 $Subgroup2 [1] 81 78 5

Hi, I want to do a meta-analysis with count data for treatement/control cases. Mi problem is that I need to use zero values (an informative zero value) for the mean and standard deviation for one of the treatement, but R has a problem: "Studies with zero values for sd.e or sd.c get no weight in meta-analysis". I can agroup the case by Family (byvar=Family). ¿Can you help me? Thanks!

Hi! I want to do a meta-analysis with count data for treatement/control cases. Mi problem is that I need to use zero values (an informative value) for the mean and standard deviation for one of the treatement, but R has a problem: "Studies with zero values for sd.e or sd.c get no weight in meta-analysis". I can agroup the case by Family (byvar=Family). ¿anybody help me? Thanks! >

Hi, ? Is there some easy way to split the grid.newpage() into two columns? For example, how could I put the two forest plots below (meta1 and meta2) next to each other? library(meta) data(Olkin95) meta1 <- metabin(event.e, n.e, event.c, n.c,data=Olkin95, subset=c(41,47,51,59),sm="RR", meth="I",studlab=author) meta2=meta1 meta2$studlab=rep("",length(meta1$studlab)

Hi All, I have conducted a meta analysis using the metabin function. I want to plot 5 subgroups on the same forest plot. I have managed to do this using the byvar argument but when i plot the forest plot in R graphics I am unable to view the very top and very bottom of the image. It is as though the plot is too long. Is there a way in which I can ask R to show the entire plot within the

Hi, I am struggling to create a 2 by 2 multiple graphs in one page. I used par(mfrow=c(2,2)) to divide the screen into 4. In each screen I draw a pie chart (They are all same). For example, my data is like this Concentration value A1 69 A2 8 G1 51 G2 1

Hi, I am struggling to create a 2 by 2 multiple graphs in one page. I used par(mfrow=c(2,2)) to divide the screen into 4. In each screen I draw a pie chart (They are all same). For example, my data is like this Concentration value A1 69 A2 8 G1 51 G2 1

I am stuck on trying to get the Lattice xyplot to output a separate PNG file each time through my 'for' loop. The files get produced but are empty. Here is the code. I'm running 1.9 on Windows. BTW is there a more efficient way of creating the separate output files than looping over the levels and subsetting? ......................................................... lev <-

Hi, I want to draw a pie chart of level (variable) for each ID (variable). I have a big dataset, here I attach part of it. ID level 1 G1 1 A1 1 A1 1 G1 1 G1 1 G1 1 A1 1 A1 1 G1 1 G1 1 G3 1 A1 1 G1 1 A1 1 A1 1 A2 1 A2 1 M 1 A1 1 G1 1 A1 1 1 1 A2 1 G3 1 A1 1 A1 1 A1 Below is my r code: level <- na.exclude(level) level.table <- table(level) level.sum <-

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

Hi, I want to produce a pie chart with titles. I used the following code. So it created a pie chart for the percentage of the score for ID="002". ID <- c("001","001","002","002","002","003","003","003","004","004","005","005","006") test <-

Hi there, I trying to solve this problem for the whole day not going anywhere, so I really hope maybe somebody can help me in this community... I've got an object coefficient2 which I want to plot in differerent ways, with colors and labels added to the points, but somehow there seems to be a problem if a value is NA within the independent variable, resulting in false labels and false colors

Hi Dataframe fdf contains bin rate overlay 1 1 90 Assign First/cc _from_SN_53 RNC_20_to_SN_50 RNC_21_Success Rate 2 2 93 Assign First/cc _from_SN_53 RNC_20_to_SN_50 RNC_21_Success Rate 3 1 90 Assign First/cc _from_SN_50 RNC_21_to_SN_53 RNC_20_Success Rate 4 2 94 Assign First/cc _from_SN_50 RNC_21_to_SN_53

Hi Dataframe fdf contains bin rate overlay 1 1 90 Assign First/cc _from_SN_53 RNC_20_to_SN_50 RNC_21_Success Rate 2 2 93 Assign First/cc _from_SN_53 RNC_20_to_SN_50 RNC_21_Success Rate 3 1 90 Assign First/cc _from_SN_50 RNC_21_to_SN_53 RNC_20_Success Rate 4 2 94 Assign First/cc _from_SN_50 RNC_21_to_SN_53

Estimado Gracias por su ayuda José 2023-05-06 19:47 GMT-04:00, Carlos Ortega <cof en qualityexcellence.es>: > Hola, > > Si añades lo que destaco, defines el layout y queda más ajustado en la > pantalla. > > #-------------------------- > library(readxl) > Sistolica <- read_excel("./Sistolica1.xlsx") > library(meta) > m <- metacont( >

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, I experimented the Metafor and Meta packages in the scope of replacing Excel for meta-analysis. I performed the first working example provided in Michael Borenstein's book "Introduction to Meta-Analysis" with Excel, Metafor and Meta. The numbers given by my spreadsheet, which I validated from Borenstein's book, conrespond quite closely to those given by Meta, but

Estimados Como logro en el forest plot que el gráfico quede en unos margenes que se pueda ver completo. Adjunto archivo saludos José library(readxl) Sistolica <- read_excel("000 EN DISEñO/0000 eli cardio 2/0000 para metanalisis/Sistolica.xlsx") library(meta m <- metacont(Ne, Me, Se, Nc, Mc, Sc, studlab=paste(Author, year), forest(m) funnel(m) ------------ próxima

Df contains Session_Setup DCT RevDataVols_bin counts comp 1 Session_Setup RLL 1 NA Session_Setup+RLL+1 2 Session_Setup RLL 2 NA Session_Setup+RLL+2 3 Session_Setup RLL 3 NA Session_Setup+RLL+3 4 Session_Setup RLL 4 NA Session_Setup+RLL+4 5 Session_Setup RLL 5

hi Jay, Consult a local statistician. Statistics is not you think is (namely simple computations, R and probably plotting..). regards, vito Jay Zola <jayjay.1988 at hotmail.nl> ha scritto: > Hello, > > > I am medical student, writing a meta-analysis on complication and > reoperation rates after the five most common treatments of distal > radius fractures. I have