similar to: Activity budgets with multiple proportions as response

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:

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.

Hi R experts :) I'm trying to carry out a survival model on my data, but I am unsure of whether it's appropriate or if I should do something specific in regards to hazard. My data is time to death by predator where I have 8 prey and one predator in the setting. This means that two prey can't possibly die at the same time and I can't quite get my head around how to include this in

This may sound crazy but... I have data like this... > results.matrix [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [1,] 949 93 2 11 26 20 7 6 10 5 0 3 [2,] 1233 124 24 35 58 57 17 21 31 19 11 21 Which is the result of binning (summing) the response variables of an underlying (nearly) continious range of predictor

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 Sergio Doing those tests 30 times is going to give you a huge Type I error rate, even if there was a function that did that. There is a reason why TukeyHSD doesn't make it easy. In general, if there are useful comparisons among the species, you are better off setting up and testing contrasts than doing all-pairwise Tukey tests. Also, the PCA scores are ordered in terms of variance

Hi, I have a data set collected from 10 measurements (response variables) on two groups (healthy and patient) of subjects performing 4 different tasks. In other words there are two fixed factors (group and task), and 10 response variables. I could analyze the data with aov() or lme() in package nlme for each response variable separately, but since most likely there are correlations among the 10

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 have searched and failed for a program or script or method to conduct a power analysis for a MANOVA. My interest is a fairly simple case of 5 dependent variables and a single two-level categorical predictor (though the categories aren't balanced). If anybody happens to know of a script that will do this in R, I'd love to know of it! Otherwise, I'll see about writing one

This is both a statistics and R question. I will give more detail if necessary. What packages of functions would help me to create a mutli-response model with multiple factors, where the number of levels for each factor is different? I have a couple hundred simulation experiments, and each experiment has four defining factors and 4 continuous and possibly correlated response variables. I am

Dear list, I have been using multivariate multiple regression (MMR) in the form lm(Y~X) where Y and X are matrices of response and predictor variables. I know that summary(mlm.object) would give the usual lm statistics for each response variable separately and that anova.mlm(mlm.object) will give the analysis of variance table of the mlm object. However, anova.mlm (also manova(mlm.object))

Dear list, I have a matrix Y of multiple response variables and a matrix X of predictor variables and I would like to fit a multivariate multiple regression model and compute the R2-value to determine the overall proportion of variance of the response matrix Y that is explained by the predictor matrix X. I have been using manova(Y ~ X) to assess the significance of the linear model. I am also

Hello everybody After doing a MANOVA on a bunch of data, I want to be able to make some comment on the amount of variation in the data that is explained by the factor of interest. I want to say this in the following way: XX% of the data is explained by A. I can acheive something like what I want by doing the following: X <- structure(c(9, 6, 9, 3, 2, 7), .Dim = as.integer(c(3,

Dear all, I'm testing the effect of species and sex in my sample by using the principal component scores of a PCA analysis. I have 30 PCs and I tried to see if there is any significant difference from males to females, given that there is a significant effect of phylogeny (factor with several species). I didi it like this: Y<-PCA$pc.scores[,1:30] fit <- manova(Y ~ sp*sex)

Hello! I'm kind of new to Linux so I'm in learning process. The thing keeping me tied to Windows is that I can't play the same games on Linux so I have been dual-booting it on my laptop. Recently I found out about Wine and decided to try it out. I decided to install Lineage 2 using Wine. Before doing anything wanted to make sure that I know exactly what to do and how so I searched

Greetings ~ I need some assistance determining an appropriate approach to analyzing multivariate binary response data, and how to do it in R. The setting: Data from an assay, wherein 6-hours-post-fertilization zebrafish embryos (n=20, say) are exposed in a vial to a chemical (replicated 3 times, say), and 5 days later observed for the presence/absence (1/0) of defects in several organ systems

Dear all, Does someone know an R function implementing the method of Sison and Glaz (1995) (see full ref below) for computing Simultaneous confidence intervals for multinomial proportions? As alternative method, I think to boostrap the mean of each proportion and get in that way confidence interval of the mean. I observed 21 times a response that could be one out of 8 categories

This is partly an R and partly a general statistics question. I'm trying to get confidence intervals of proportions (sometimes for subgroups) estimated from complex survey data. Because a function like prop.test() does not exist for the "survey" package I tried the following: 1) Define a survey object (PSU of clustered sample, population weights); 2) Use svyglm() of the package

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

Dear Md Kamruzzaman, I've copied this response to the r-help list, where you originally asked your question. That way, other people can follow the conversation, if they're interested and there will be a record of the solution. Please keep r-help in the loop See below: On 2024-01-17 9:47 p.m., Md. Kamruzzaman wrote: > > Caution: External email. > > > Dear John >