similar to: self-written function

I have some question about metafor package. I'm interest to perform a random effect meta-analysis of proportion (single group summary of prevalence of disease in a population as reported by different study) It ask: 1. "PFT": The Freeman-Tukey double arcsine transformed proportion is reported to be equal to 1/2*(asin(sqrt(xi/(ni+1))) + asin(sqrt((xi+1)/(ni+1)))). Hovewer, i

R-Experts: Does anyone know if there are R functions to perform the Freeman-Tukey double arcsine transformation and then backtransform it? Thanks, Brant Inman Mayo Clinic

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

I am tryin to perform an arcsine transformation on my data containig percentages as the dep. variable. Does anyone have a code that I could use to do that? I am relatively new to R. Thanks for your help! -- View this message in context: http://r.789695.n4.nabble.com/Arcsine-transformation-tp4676706.html Sent from the R help mailing list archive at Nabble.com.

Hi everyone, I'm using the metafor package to meta-analyze a set of proportions. This is working really well for the raw proportions, but is there a way to back-transform the arcsine transformed proportions in the rma or forest functions with the atransf option? The estimates and CIs for the transformed proportions need to be back-transformed to be the sin of the estimate squared.

I have question about log2 transformation and performing mean on log2 data. I am doing analysis for ELISA data. the OD values and the concentration values for the standards were log2 transformed before performing the lm. the OD values for samples were log2 transformed and coefficients of lm were applied to get the log2 concentration values. I then backtransformed these log2 concentrations and the

Dear R users, suppose I have a matrix of observations for which I calculate all pair-wise correlations: m=matrix(sample(1:100,replace=T),10,10) w=cor(m,use="pairwise.complete.obs") How do I extract only those correlations that are >0.6? w[w>0.6] #obviously doesn?t work, and I can?t find a way around it. I would very much appreciate any help! Best wishes Christoph (using R

Hi Everyone, I'm a pretty useless r-er but have data that SPSS etc doesn't like. I've managed to do GLMs for my data, but now need to fit a thin plate spline for my data (arcsine.success~date.num:clutch.size) If anyone has a bit of spare time and could come up with a bit of code I'd be very grateful- I just don't get R language! Thanks Rach -- View this message in context:

I have been trying to preform both a bartlett's test and an arcsine transformation on some average percentage data. I've tried inputting it different ways and I keep getting the same error message: > head(workingdata) DYAD BEFORE AFTER 1 BG-FL 4.606772 5.787520 2 BG-LL 5.467503 7.847395 3 AD-MV 5.333735 11.107380 4 MM-FL 5.578708 12.063500 5 MM-MV 2.037605 6.415303 6 MM-RM

Dear all, I have a data frame that looks like this: c1 c2 c3 A B C B C A A A B and so on; I?d like to produce one single vector consisting of the columns c1,c2, c3, such that vector=("A","B","A","B","C","A","C","A","B") I guess it?s easy to do but I don?t know how...Can anyone

Dear all, How can I replace text in objects that are of class "formula"? y="a * x + b" class(y)="formula" grep("x",y) y[1] Suppose I would like to replace the "x" by "w" in the formula object "y". How can this be done? Somehow, the methods that can be used in character objects do not work 1:1 in formula objects... Many

Dear all, For an introductory course on glm?s I would like to create an example to show the difference between glm and transformation of the response. For this, I tried to create a dataset where the variance increases with the mean (as is the case in many ecological datasets): poissondata=data.frame( response=rpois(40,1:40), explanatory=1:40) attach(poissondata) However, I have run into

Dear R users, Let?s say I have 10 models, each named m1,m2,m3..., and I would like to summarize them automatically and simultaneously - e.g., to extract parameter estimates later on from all models; how can I do that? I have tried: x=1:10 #this creates some example data y=rnorm(10) m1=lm(x~y) m2=lm(x~1) sum.lms=function(x)summary(paste("m",x,sep="")) sum.lms(1:2) but

Dear R users, I??ve got a simple question but somehow I can??t find the solution: I have a data frame with columns 1-5 containing one set of integer values, and columns 6-10 containing another set of integer values. Columns 6-10 contain NA??s at some places. I now want to calculate (1) the number of values in each row of columns 6-10 that were NA??s (2) the sum of all values on columns 1-5

Dear R users, I am currently analyzing a dataset using lme(). The model I use has the following structure: model<-lme(response~Covariate+TreatmentA+TreatmentB,random=~1|Block/Plot,method="ML") When I plot the residuals against the fitted values, I see a clear positive trend (meaning that the variance increases with the mean). I tried to solve this issue using weights=varPower(),

Dear all, Is there a way to retrieve standard errors from nls models? The help page tells me that arguments such as se.fit are ignored... Many thanks and best wishes Christoph -- Dr. rer.nat. Christoph Scherber University of Goettingen DNPW, Agroecology Waldweg 26 D-37073 Goettingen Germany phone +49 (0)551 39 8807 fax +49 (0)551 39 8806 Homepage http://www.gwdg.de/~cscherb1

Dear all, I stumbled over a problem recently when trying to use srt with text() on a windows device. What I intended to do was to plot a simple regression line, and to rotate a piece of text such that the text has the same angle as the regression line. However, the text is always plotted in a slightly wrong angle: #### x=1:10 #create arbitrary x and y values y=x*2-rnorm(1:10)

Hello everyone, I am working for a few days already on a basic algorithm, very common in applied agronomy, that aims to determine the degree-days necessary for a given individual to reach a given growth stade. The algorithm (and context) is explained here: http://www.oardc.ohio-state.edu/gdd/glossary.htm , and so I implemented my function in R as follows: DD <- function(Tmin, Tmax, Tseuil,

Dear all, I got this error message > library(dprep) > mardia(Savg) Error in cov(data) : 'x' is empty But with the same data, I got > library(mvnormtest) > mshapiro.test(Savg) Shapiro-Wilk normality test data: Z W = 0.9411, p-value = 0.6739 What does the error message "Error in cov(data) : 'x' is empty" mean? Thanks a lot! Jiao

1. If your code actually runs, you should upgrade R, and quit using `_' for assignment... 8-) 2. You seem to have an extraneous `]' after the na.exclude. Could that be the problem? Andy > From: Christoph Scherber > > actually, the situation is much more complicated. I am producing > multiple graphs within a "for" loop. For some strange reason, the >