search for: arcsin

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 ma...

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 t...

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-...

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!=&quot...

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]]

...implemented my function in R as follows: DD <- function(Tmin, Tmax, Tseuil, meanT, method = "DDsin") ### function that calculates the degree-days based on ### minimum and maximum recorded temperatures and the ### minimal threshold temperature (lower growth temperature) { ### method arcsin if(method == "DDsin"){ cond1 <- (Tmax <= Tseuil) cond2 <- (Tmin >= Tseuil) amp <- ((Tmax - Tmin) / 2) print((Tseuil-meanT)/amp) alpha <- asin((Tseuil - meanT) / amp) DD_ifelse3 <- ((1 / pi) * ((meanT - Tseuil) * ((pi/2) - alpha)) + amp*cos(alpha)) DD...

...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.

...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 transfer the skewed distribution data into normal distribution data, but failed. So what I can do is only find a calculation method to do with skewed data. Any suggestions from you are welcome. Thanks a lot for your help. My best. [[alternative HTML version deleted]]

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 &quo...

...re. 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 (bolt). My data is not normal and doesn't become normal after ArcSin or Log transformations. Because the 'Days' has 4 levels, I performed a Kruskal-Wallis (kruskal.test) and got significant results. Same for comparing 'Cookie'. Neither of these have only 2 levels so significance here doesn't really explain it in depth....but everything I'...

...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....

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*SP2COV...

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 int...

...rials example: 1) groupedData is used to specify the nesting structure, 2) a GLS model is fit assuming no spatial autocorrelation, 3) spatial covariance is added using the Variogram function, and 4) spatial correlation structures are compared. Before beginning, "canopy" (a percentage) is arcsin sqareroot transformed to "trancano", and "replicat" is specified as a factor. > smith<-read.table("C:\\Users\\matlack\\Desktop\\Documents\\Undergrad Research\\Nicole Smith\\smith.txt",header=T) > attach(smith) > trancano=(asin(sqrt(canopy/100))) > repli...