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...file just for European women from the original database and conducted the following: women_west<-read.csv("women_west.csv") print(women_west) dat<-escalc(measure="ZCOR",ri=Pearson,ni=N,data=women_west,append=TRUE) res<-rma(yi,vi,data=dat) is.factor(dat$year) forest(res,transf=transf.ztor) In meta-regression, I used the original database, but used categorical moderators for sex (=women), and ethnicity (=european) to find the effect specifically in European women. adult<-read.csv("adult.csv") print(adult) dat<-escalc(measure="ZCOR",ri=Pearson,ni...

...ist()) With PosOutc being the column containing the raw correlations and N being the column with the sample sizes. I assume that this will imply that a) although the 'tau2' value is set to NULL, still a random-effects analysis is calculated b) the raw correlation coefficients are transformed to fisher-z-values c) the according variances of the fisher-z-values are calculated from the sample sizes d) these calculated z-values and sample sizes are then used to calculate summary effect and confidence interval limits e) the resulting object contains z-values and not raw correlations...

...ssing studies that would bring p-value to the alpha, to be exact)was different than that I got in Comprehensive Meta-Analysis Version 2.0. I wonder why R and CMA got different results. *Below is the R code:* dat=read.table("Your working directory\\Example.csv",header=T,sep=",") transf.ies=escalc(xi=cases,ni=total,measure="PLO",data=dat) #I transform the data using the logit transformation first. In CMA, it also uses the logit transformation. transf.pes=rma(yi,vi,data=transf.ies,method="DL",weighted=TRUE) #Pooling individual effect sizes in the logit scale. ra...

...he tranformes correlation coefficients for the categorical moderator variables? Questions 2: How to calculate the average reliability coefficients for each category of the moderator variable? Take the reliablity generalization as an example. Here is an example of my code. #meta-analysis using transformed alpha values (using the transformation suggested by Bonett, 2002) res<-rma(measure="ABT", ai=ai, ni=size, mi=n_items, dat=data) res backtransf_res<-predict(res, transf=transf.iabt) backtransf_res #meta-regression of categorical variables #take var1 as an example of categorica...

Hello, I am performing a meta-analysis using the metafor package. My data are proportions and I used the Freeman Tukey double arcine (FT) transformation to fit the random effects model. Now I want to create a forest plot with my estimates backtransformed to the original scale of proportions. Can this be done? Regards, Patricia ------------------------------------------------------------------------------ De informatie opgenomen in dit be...

...ng p-value to the alpha, to be exact)was different than that I >got >in Comprehensive Meta-Analysis Version 2.0. I wonder why R and CMA got >different results. > >*Below is the R code:* >dat=read.table("Your working directory\\Example.csv",header=T,sep=",") >transf.ies=escalc(xi=cases,ni=total,measure="PLO",data=dat) #I transform >the data using the logit transformation first. In CMA, it also uses the >logit transformation. >transf.pes=rma(yi,vi,data=transf.ies,method="DL",weighted=TRUE) #Pooling >individual effect sizes in the...

...msum(c(0,new)), col="grey75") # the original values axis(1, at=cumsum(0:10), line=-5) # the 'breaks' for the new vector # CODE STOP The grey lines show how area from the vector 'orig' should fall into the vector 'new'. I want to represent this relationship as a transformation matrix (transf.mat) of dimensions length(new) x length(orig) such that each element in the matrix represents the shared areas between the vectors. colSums(transf.mat) therefore is identical to orig and rowSums is identical to new. I've worked transf.mat out by hand for the example a...

Hi Dr. Viechtbauer, I'm doing meta-analysis using your package 'metafor'. I used the 'IRFT' to transform the incident rate. But when I tried to back-transform the summary estimates from function rma, I don't know what's the appropriate ti to feed in function transf.iirft. I searched and found your post about using harmonic mean for ni to back-transform the double arcsine transformation. I...

...oblems when trying: Firstly, I can enter two random factors in lmer but I do not get complete outputs using both anova () and summary (). For instance, I only get df, SS, and MS using anova(). Even so the model runs without giving an error message. > lmer.inspection <- lmer(duration.root.transf~sex*sample.sex+age+sample.age+sample.type+first.bout+location+sample.source + (1|acceptor.grp) + (1|donor.grp)) > anova (lmer.inspection) Analysis of Variance Table Df Sum Sq Mean Sq sex 1 0.06653 0.06653 sample.sex 1 0.60389 0.60389 Running the same model in...

Bonjour, J'ai un petit souci avec mon petit couple rsync over ssh. Quand je fais un rsync d'une machine, et que celle-ci dispose d'une messagerie de type vpopmail, le backup m'indique ? la fin que certains fichiers n'ont pu ?tre transf?r?s. Ceci est du au fait que sachant que les services POP/SMTP sont actif, des fichiers ont ?t? supprim?s entre le temps ou la liste des fichiers a ?t? r?alis?e par rsync et le temps ou rsync est arriv? jusqu'a ces fichiers pour les transf?rer. Quelqu'un aurait-il une astuce pour contourne...

...ssing studies that would bring p-value to the alpha, to be exact)was different than that I got in Comprehensive Meta-Analysis Version 2.0. I wonder why R and CMA got different results. *Below is the R code:* dat=read.table("Your working directory\\Example.csv",header=T,sep=",") transf.ies=escalc(xi=cases,ni=total,measure="PLO",data=dat) #I transform the data using the logit transformation first. In CMA, it also uses the logit transformation. transf.pes=rma(yi,vi,data=transf.ies,method="DL",weighted=TRUE) #Pooling individual effect sizes in the logit scale. ra...

...5714 48 3609 4 Mwanzia et al. 2011 4 2 8971 0 100 > dat<-escalc(measure="RR", ai = mosq1pos, bi = mosq1neg, ci = mosq2pos, di = mosq2neg, data = mixed, append = TRUE) > res<-rma(yi, vi, mods = ablat, data=dat) > predict(res,transf = exp, addx = TRUE) > preds<-predict(res, transf = exp) > wi<-1/sqrt(dat$vi) > size<-0.5 + 3 *(wi-min(wi))/(max(wi)) - min(wi) > plot(dat$ablat, exp(dat$yi), pch = 19, cex = size, xlab = "Absolute Latitude", ylab = "Relative Risk", las=1, bty = "l"...

Dear All, I'm having trouble tweaking a forest plot made using the R meta-analysis package metafor. I did the analysis based upon the correlation coeff from studies and plotted the corresponding forest plot easily > q2<-rma(yi,vi,mods=cbind(grupo),data=qim) > q2 > forest (q2,transf=transf.ztor,digits=3, ... ,alim=c(0,1),refline=.5) > text(-1.55,42,"Esp?cie [ref?]",pos=4,font=2) I'd like to subdivide the "table" by the moderator 'grupo' - i.e. create the necessary spacings in the list of studies to accommodate the moderator statistics (c...

...ontributors()' for more information. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for a HTML browser interface to help. Type 'q()' to quit R. [Previously saved workspace restored] > source("convert.r") > convert("transf") Error in file(file, "r") : unable to open connection In addition: Warning message: cannot open file `transf.cgm' > ----------------------------------- Have i done some mistakes?Or i am missing some files? Please assist. Rgs, Orlando. [[alternative HTML version deleted...

...mbination of the first three parameters to sum up to 1 (which obviously do not) s<-p1[,1]+p1[,2]+p1[,3] s==1 It occurred to me to divide each of these parameters with the sum (vector "s" above). However the uniform distribution is lost (example for parameter 1 - first column): par1.transf<-p1[,1]/s hist(par1.transf) So, is there a way to maintain the random LHS (with uniformly distributed parameters) so that the refered condition is fulfilled? Any suggestions would be much welcome. Thanks, Duarte

ok, a other try .. i tranfer the roles back to DC2: first run: error second run: error third run: NO error then i added "import samba.drs_utils" in /opt/samba/lib/python2.7/site-packages/samba/netcmd/fsmo.py and now the role transfer works fine. root at dc2:~# samba-tool fsmo transfer --role=all -U administrator FSMO transfer of 'rid' role successful FSMO transfer of 'pdc' role successful FSMO transfer of 'naming' role successful FSMO transfer of 'infrastructure' role successful FSMO transf...

...lue (the y), seems to inspect the inner code of the argument function in a way that even R itself doesn't do. See what I'm talking about: # A function that returns a 2-element vector, given a # single argument zetas <- function(alpha) {z <- qnorm(alpha/2); c(z,-z)} # A transformation function - it can take a vector as # its z argument Tzx <- function(z, sigma_p, mu_p) sigma_p*z + mu_p # Another transformation function similar to the # previous one - it can take a vector as its x argument Txz <- function(x, sigma_p, mu_p) (x - mu_p)/sigma_p # Th...

...ure where the observations have an exponentially decaying weighting rather than the equal weighting implied by a standard MLE approach. The optimization function which leads to my parameter estimates is given by; gh.fit = try(optim(vega, negllh,hessian = se,pdf = gh.pdf, tmp.data = data, transf = transform, const.pars = vars[!opt.pars], silent = silent, par.names = names(vars), ...)) param.est = gh.fit$par In the above, vega are the parameters to estimate and pdf is the GH pdf. This seems to work well for the case where observations are equally weighted. However, I'm stuck...

Dear everyone I am a bit of a computer imbecile and are having problems with R. I am using R in my research project to do chi-square tests on data imported from excel . However I have som missing data in one of my variables (columns) and I need R to "exclude" these and make chi-square test on the data that I have. I use a formula to make 2x2 tables which is: data <-

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