similar to: chi-square and error bars?

Dear All, I have a problem understanding the difference between the outcome of a fisher exact test and a chi-square test (with simulated p.value). For some sample data (see below), fisher reports p=.02337. The normal chi-square test complains about "approximation may be incorrect", because there is a column with cells with very small values. I therefore tried the chi-square with

I have some questions about the "Continious Repo". Does the "Continious Repo" replace the Centos-Base/Updates repo? Is the "Continious Repo" in a standard place on the CentOS mirrors? Ie. can I just add another line to my rsync script to get this repo? It is not possible for me to just update directly off the net (I have a slow and unreliable network connection)

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 Dr. Viechtbauer, I'm doing meta-analysis using your package 'metafor'. I used the '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

Hello, I have a contingency table showing relation between two datasets. I tried to see association among them with the assocplot, but it shows error. mosaicplot of the same data worked perfectly. Can anyone please help me. Con.table=as.matrix(Con.table) > dim(Con.table) [1] 27 27 > assocplot(Con.table,space = 0.3, + main = NULL, xlab = NULL, ylab = NULL) Error in

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.

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?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)

Hi! Can anybody help me with convert Cisco 7940 CallManager Phone to a SIP Phone? I have continious error in tftp log: connect from 192.168.1.111 Mar 18 12:12:30 AKrasavin utftpd[10081]: peer requests OS79XX.TXT, conversion octet Mar 18 12:12:30 AKrasavin utftpd[10081]: unterminated option value in init packet Mar 18 15:12:30 AKrasavin xinetd[10068]: START: tftp pid=10080 from=192.168.1.111 Mar

I have a set of labels arranged along a timeframe in a. Each label has a timestamp and marks a state until the next label. The dataframe a contains 5 such timestamps and 5 associated labels. This means, on a continious scale between 1-100, there are 5 markers. E.g. 'abc' marks the timestampls between 10 and 19, 'def' marks the timestamps between 20 and 32, and so on. a <-

I have two kind of variables. One kind where the values are continious and ranges from -1 to 5, and the other is boolean (i.e. true or false). For example: V1   V2  V3 V4 V5  V6 0.1  0.4   1    0   -1   3.7 0.4  0.1   2   -1   0    3.7 and V1  V2  V3 0     1    0 0     0    1 I want to make a model for classification, combining this two. Will it be wiser to use the second variable values

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

Hello, While doing some enrichment tests using chisq.test() with simulated p-values, I noticed some strange behaviour. The computed p-value was extremely small, so I decided to dig a little deeper and debug chisq.test(). I noticed then that the simulated statistics returned by the following call tmp <- .Call(C_chisq_sim, sr, sc, B, E) were all the same, very small numbers. This, at first,

It is not about "really arge total number of observations", but: set.seed(4711);tabs <- r2dtable(1e6, c(2, 2), c(2, 2)); A11 <- vapply(tabs, function(x) x[1, 1], numeric(1));table(A11) A11 0 1 2 166483 666853 166664 There are three possible matrices, and these come out in proportions 1:4:1, the one with all cells filled with ones being most common. Cheers, Jari

Hi, I am currently trying to do a GLMM on a dataset with percent cover of seagrass (dep. var) and a suite of explanatory variables including algal (AC) and epiphyte cover (EC), rainfall, temperature and sunshine hours. M2=glmer(SG~AC+EC+TP+SS+RF+(1|Location/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

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))~

Dear list, I would like to fit a negative binomial regression model as described in "Byers AL, Allore H, Gill TM, Peduzzi PN., Application of negative binomial modeling for discrete outcomes: a case study in aging research. J Clin Epidemiol. 2003 Jun;56(6):559-64" to my data in which the response is count data. There are also 10 predictors that are count data, and I have also 3

Hi, I'm running a tutorial ("Meta-analyses of data from two (or more) microarray data sets"), which use wgcna package. I have an error in the function modulePreservation (it is below). I'm using R2.13 Can you help me? Do you know, what is happens? Thanks Raquel multiExpr = list(A = list(data=t(badea)),B = list(data=t(mayo))) # two independent datasets (dim = 13447 x 36) mp =