search for: jokel

...package. I would like to extract values of the confidence intervals of the effect sizes of the single studies from an rma object. Those values are printed out when plotting a forest plot using the forest function on the rma object, however I was not able to locate them. Many thanks for your help! Jokel [[alternative HTML version deleted]]

...itiy, I am trying to use multiple functions for aggregation within a function call for dcast. However this seems to result in an error. Also I have not managed to make dcast() work with fun.aggregate=sd. Please find attached some example code using the ChickWeight data. Many thanks for your help! Jokel #Chick weight example names(ChickWeight) <- tolower(names(ChickWeight)) sd(ChickWeight$weight) # works fine mean(ChickWeight$weight) # works fine length(ChickWeight$weight) # works fine chick_m <- melt(ChickWeight, id=2:4, na.rm=TRUE) dcast(chick_m, time~variable, mean) # works fine...

...f two variables: SD=SQRT(SD_A^2 + SD_B^2 + 2*r*SD_A*SD_B) with SD_B and SD_B being the standard deviation of A and B. And r*SD_A*SD_B being the covariance of A and B. Would this formula also be valid if I want to average (and not sum) my two variables? Many thanks for any help & best wishes, Jokel [[alternative HTML version deleted]]

...ould need n=400 subjects (200 in each group). This is seems so incredibly high that I mistrust my results & wanted to ask whether I miscalculated n? library(pwr) pwr.t.test(d=0.28,sig.level=0.05,power=0.8,type="two.sample",alternative="two.sided") Many thanks for you help! Jokel [[alternative HTML version deleted]]

...one group in the other studies (B=C,D). Is there a way? The handbook of research synthesis and meta-analysis By Harris M. Cooper says that: d=sqrt((F*(n1+n2)/n1*n2)) for ANOVA (one-factor with two-levels) but does this also hold for ANOVA (one-factor with three levels)? Many thanks for your help! Jokel [[alternative HTML version deleted]]

...amp; B)? However I am interested in the main-effects and the interaction of these two covariates and the dummy-coding would only allow to detect the effect of one level of one factor. Would there be a way to assess main-effects and interactions (something like an meta-analysis-ANOVA)? Many thanks, Jokel [[alternative HTML version deleted]]

...pos=4) text(6, 15, "Relative Risk [95% CI]", pos=2) par(op) Is there a way to define the colum names' position relatively to the plot so they would be adequately positioned no matter what is the number of studies included in the plot? Many thanks for your help!, Jokel [[alternative HTML version deleted]]

Dear R-List! I would like to embed R code in an online database such as i.e. a google spreadsheet in way that users can add data to the database and that R's calculations are updated automatically and i.e. given out in the spreadsheet. Maybe even graphs could be updated online? Is there a way to implement this? Many thanks! J. [[alternative HTML version deleted]]

Dear R-List! Is there any function (apparently for BioStat there is!) to calculate effect sizes with the p-values and sample sizes? That would be really useful for meta-analysis! Thanks for the help! Jokel

...ting the set into two sets using one set for training of the svm and the other for testing the predictive power of the trained svm. My understanding would be that (A) and (B) are alternative ways to estimate the performance of the svm. Or would I have to implement both? Many thanks for your help! Jokel [[alternative HTML version deleted]]

...y to use first (A) then do grid-search (via the tune command) to find the best hyperparameters and then test the trained svm by applying it to the test set? And in case I use a linear kernel instead of RBF, I guess I do not need to run grid-search as there are no hyperparameters to estimate? BEst, Jokel [[alternative HTML version deleted]]

...factor and control for re-test effects by including 'session'. Could you advice me regarding the proper implementation of my model? Also I am happy about any pointers to R-related literature. Please find below some dummy data and initially model formulation. Many thanks & best wishes, Jokel # some dummy data data <- structure(list(subject = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L), .Label = c("1", "2", "3", "4", "5"), class = "factor&quo...