search for: backtransform

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 bericht kan vertrouwelijk zijn en is uitsluitend bestemd voor de geadresseerde. Indien u dit bericht onterecht...

...rming 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 trouble started. when i take the mean of log2 concentrations the value is different than the backtransformed concentrations. > 100+1000/2 [1] 600 > 2^((log2(100)+log2(1000))/2) [1] 316.2278 What I am doing wrong to get the different values -- View this...

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

...sqrt(xi/(ni+1))) + asin(sqrt((xi+1)/(ni+1)))). Hovewer, i also found the same formula but without 1/2*. 2. how "vi", the corresponding (estimated) sampling variance is calculated? (i.e. the formula used to estimate "vi") 3. it is possible to return a forest plot with the backtransformed value of proportion, confidenc interval and weight and not with the transformed value after performing rma.uni? Many thanks Mario Petretta Dipartimento di Medicina Clinica Scienze Cardiovascolari e Immunologiche Facolt? di Medicina e Chirurgia Universit? di Napoli Federico II 081 - 7462233

...on from: (NULL) (129.215.190.229) When using predict.Arima in library ts(), it appears differencing is only accounted for in the first step of prediction and so any trend is not apparent in the predictions. The example shows the difference between the predictions of an arima(1,1,1) model and the backtransformed predictions of an arima(1,0,1) model fitted on the differenced data. Example: total <- c(750, 775, 971, 1099, 1344, 610, 910, 1056, 1589, 1006, 1469, 1598, 876, 1104, 1197, 1062, 1783, 1554, 1400, 1340, 1013, 1031, 1030, 860, 943, 1542, 1400, 1301, 1443, 1057, 1296, 710, 1038, 1447, 694, 889...

...lt;-rep(0,l) #Cell probabilities p[1]<-h[1] prod<-(1-h[1])*S[1] for (i in 2:(l-1)){ p[i]<-prod*h[i] prod<-prod*(1-h[i])*S[i] } p[l]<-1-sum(p[-l]) #last cell probability -dmultinom(exp(d),prob=p,log=TRUE) #exp(d)->backtransform the estimates } c<-c(cohort,100) #untransformed initial values for the optimization,->100=N-x1-x2-x3 nvec<-c(rep("x",l-1),"N") #names for the initial vector nrs<-c(1:(l-1),1) #nrs for the initial vector svec = log(c) #transform...

Dear all, I?ve got a simple self-written function to calculate the mean + s.e. from arcsine-transformed data: backsin<-function(x,y,...){ backtransf<-list() backtransf$back<-((sin(x[x!="NA"]))^2)*100 backtransf$mback<-tapply(backtransf$back,y[x!="NA"],mean)

Dear list, could someone point me to the normal score transform and it's use in R? I would like to transform my data with normal score transform, do some geostatsitical predictions and would like to transform the estimated values back including the estimation variance. Any suggestions? Ulrich -- __________________________________________________ Ulrich Leopold MSc. Department of

dear members, I have a heteroscedastic time series which I want to transform to make it homoscedastic by a box cox transformation. I am using Otexts by RJ hyndman and George Athanopolous as my textbook. They discuss transformation and also say the fpp3 and the fable package automatically back transforms the point forecast. they also discuss the process which I find to be

My question is related to plot( ) in the mgcv package. Before modelling the data, a few predictors were transformed to normalize them. Therefore, the x-axes in the plots show transformed predictor values. How do I back-transform the predictors so that the plots are easier to interpret? Thanks in advance, Suzan -- Suzan Pool Oregon State University Cooperative Institute for Marine

Hi   I did validation for prediction by logistic regression according to following:   validationsize <- 23 set.seed(1) random<-runif(123) order(random) nrprofilesinsample<-sort(order(random)[1:100]) profilesample <- data[nrprofilesinsample,] profilevalidation <- data[-nrprofilesinsample,] salich<-profilesample$SALIC.H.1 salic.lr<-glm(salich~wetnessindex, profilesample,

...timates oo<-nn$fixed oo[2,]<-oo[1,]+oo[2,] oo[3,]<- oo[1,]+oo[3,] pp<-exp(oo) But the means are not the right means (see speciesmeans below), the intercept does not correspond with level 1 of treatment (b1), and the confidence limits seem too big. Also, I'm not sure if I've backtransformed correctly. I've also tried directly calculating the means and CIs, but I'm not sure if the first line below is actually the right variance to use in estimating the CIs. variance<-mm$sigma #IS THIS THE RIGHT VARIANCE? speciessize<- tapply(species, burnagecat1 ,length) speci...

Hi, I can't seem to find a function in R that will reverse a BoxCox transformation. Can somebody help me locate one please? Thanks in advance. Best wishes, Des [[alternative HTML version deleted]]

Hello! Thank you for answering! What I am trying to do is to plot my raw values (biomass of different species) on a logaritmic y-axis with the base of e. When I type "log="y"", the axis transforms into a logaritmic axis with the base of 10. Best regards, Elisabeth > Dear Elisabeth, > > I'm not sure if I have understood your question -- are you trying > to

Dear R community, I'm using the ,metafor'-package by Wolfgang Viechtbauer (Version: 0.5-5) to calculate random-effects meta-analyses using Correlations and Sample Sizes as the raw data. (By the way: Really a nice piece of work, Wolfgang! Thanks heaps.) I specified the "rma.uni' function so that it looks like this: MAergebnis<-rma.uni(ri=PosOutc, ni=N,

Full_Name: Dr. Hans A. Kestler Version: 1.8.1. OS: Linux, Win, Mac OSX Submission from: (NULL) (134.60.73.116) The code below produces after a different number of iterations i the following error: Error in optim(par = rep(0.5, length(edges)), loglik, method = "L-BFGS-B", : non-finite value supplied by optim This was reproducible on different machines (Mac G4 OSX, AMD Opteron