search for: arcsine

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 mai...

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

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. Back-transforming the output isn't a big deal - it's the forest plots that I would need th...

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-M...

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$back,y[x!="NA"],mean) backtransf$sdback<-tapply(backtransf$back,y[x!="NA"],stdev)/sqrt(length(y[x!="...

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: http://r.789695.n4.nabble.com/Help-Fitting-a-Thin-Plate-Spline-tp3353485p3353485.html Sent...

Hello, I have data which I have arcsin transformed to analyse. I want to plot my data with error bars however as my data is back-transformed my standard errors are uneven. Is there a simple way to draw these asymmetric error bars in R? Thanks for your help. [[alternative HTML version deleted]]

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,

...see that each row is not drawn from the same underlying population using chisq.test. What I would like to do is plot each proportion from each row with a confidence interval which represents the results of the chisq.test, i.e. put confidence intervals on each proportion. I thought about doing the arcsine transformation on the proportions and then I can use the binomial distribution to get the variance and hence 95% confidence interval (then back transformation). Clearly confidence in latter points is weaker (fewer observations), but I can't seem to find a plot to capture this fact.

Hi all: I have a question on sample size calculation of 2 groups of data. If 2 groups of data are all normal distribution, then I can use the function "n.indep.t.test.eq" from samplesize package.But if 2 groups of data are all skewed distribution, but not normal distribution,how can I calculate the sample size then? I've tried many transformation (e.g. log arcsin…) in order to

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 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 &quot...

Hi, I have searched and found a response to a question similar to mine but when I tried the code, R says it's not an actual function so I thought I'd ask here. http://r.789695.n4.nabble.com/file/n4633035/Cookies.csv Cookies.csv I have attached the data I am using. I am trying to look at two things: how moisture content changes over time, and how it changes along the length of a log

...e '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'm hoping I can get your help on ti too. Thanks. Chunyan Liu 513-636-9763 Biostatistician II Department of Biostatistics and Epidemiology Cincinnati Children's Hospital Medical Center [[alternative HTML version deleted]]

...fSi/fTr), family=binomial,data=data,nAGQ=1) As the dependent variable is percent cover, I used a binomial error structure. I also have a random effect due to nested of the data collection strategy. However, I keep getting heteroscedasticity issues as shown in the image below. I have tried using an arcsine transformation (with a lme), but the scatter of residuals are still very much similar. What else can I do to try to resolve the heteroscedasticity in my data? Any help will be very much appreciated! <http://r.789695.n4.nabble.com/file/n4719735/Heteroscedasticity.png> [http://r.789695.n...

Hello useRs! I have % cover data for different plant species in 300 plots, and I use the ARCSINE transformation (to deal with % cover data). When I use a GLM I do not have any problem. But when I am trying to use a GAM model using mgcv package, to account for non-linearity I get an ?error message?. I use the following model: sp1.gam<-gam(asin(sqrt(0.01*SP1COVER))~ s(asin(sqrt(0.01*SP2COVE...

Hello! When I am analyzing proportion data, I usually apply logistic regression using a glm model with binomial family. For example: m <- glm( cbind("not realized", "realized") ~ v1 + v2 , family="binomial") However, sometimes I don't have the number of cases (realized, not realized), but only the proportion and thus cannot compute the binomial model. I just

Dear R users, Is it reasonable to transform data (measurements of plant height) to the power of 1/4? I?ve used boxcox(response~A*B) and lambda was close to 0.25. Regards, Christoph

Hi, I am trying to do a power calculation for a difference in proportions test where I want to estimate the sample size required. I know (well estimate) that group one (n1) is 10% of the population and group 2 (n2) is 90% of the population. I know the effect size (h). pwr.2p2n.test only allows one variable to be left null whereas I would like both n1 and n2 to be determined where I know there

...0.125, Inactive, 0.125 and so on for each subject. I have two fixed effects and a possible random effect that may only have a very small influence on the end results. The four response variables are obviously not independent as they are a proportion of the total. The data are not normal, even when arcsine transformed from proportions where only the response variable with the highest proportion comes close (that being swimming). I have used four separate binomial glms with each response variable (in seconds rather than as a proportion) against total time, but I'd rather incorporate them all into...

Dear R Help, I am having difficulty using Variogram within GLS to examine spatial structure of nested data. My data frame consists of ecological measurements of a forest, in which three landscape positions ("landposi") are compared. Each landscape position is replicated five times ("replicat"), and the environment is measured ("canopy", "litdepth", etc.)