similar to: D'agostino-Pearson K2 omnibus test.

Hi, Does anyone know if the D'agostino test is available with R ? Alex

Dear useRs, I was searching CRAN for implementation of kurtosis and skewness tests, and found that there is some kind of lack on it. So, I have written three functions: 1. Anscombe-Glynn test for kurtosis 2. Bonett-Seier test based on Geary's kurtosis (which is not widely known, but I was inspired by original paper describing it, found coincidentally in Elsevier database) 3.

R 2.9.1 Windows XP I am fitting a random effects ANOVA with two factors Group which has two levels and Time which has three levels: fita<-lme(Post~Time+factor(Group)+factor(Group)*Time, random=~1|SS,data=blah$alldata) I want to get the omnibus significance tests for each factor and the interaction. I believe I can get the omnibus test for the interaction by running the model:

If one estimates a model using multinom, is it possible to perform the omnibus LR test ( the analogue to omnibus F in linear models ) using the output from multinom ? The residual deviance is there but I was hoping I could somehow pull out the deviance based on just using an intercept ? Sample code is below from the CAR book but I wasn't sure how to do it based on that example. Thanks

gem install camping-omnibus --source code.whytheluckystiff.net Considering the number of optional dependencies now in Camping, I''ve put together an eigengem which will load all of the recommended libraries that most devoted campers will want: ActiveRecord, Mongrel, SQLite3, RedCloth and the acts_as_versioned gem. (Those last two are used in the examples.) Stuff like mosquito and

I''ve noticed that the copy of Mongrel installed by the camping-omnibus gem doesn''t work with Ruby 1.8.6. Or to be more specific, cgi_multipart_eof_fix (which Mongrel is dependent upon) doesn''t work: > $ sudo gem install mongrel --source http://code.whytheluckystiff.net > > ERROR: Error installing mongrel: > cgi_multipart_eof_fix requires Ruby version

I am looking for an R package with which one can calculate an effect size for a set of contrasts given an omnibus chi-square test statistic (more than 1 degree of freedom). Is there such a package? Presumably, it would implement the procedure (or something like it) described by Roznow and Rosenthal 1996 Psychological Methods 1: 331-340. many thanks! Steven Orzack Fresh Pond Research Institute

Hi all, I'm using spatstat to investigate the spatial structure of an arid shrub population. The first-order intensity of my data does not appear to be homogenous, so I would like to use inhomogeneous techniques. I realise there is a inhomogeneous K-function available in spatstat, but there doesn't not appear to be one for the pair-correlation function (O-ring statistic). As such I was

This is very possibly not a question on R. I was under the impression that the argument that gives rise to Fisher's LSD method in ANOVA works in other situations with three-way comparisons too, given that formal logic works the same ("if the omnibus test rejects, only two of the three groups may be equal, and therefore only one hypothesis can be rejected falsely"). However, when I

Hello, I've been running some HLMs using the lme function quite happily; it does what I want and I'm pretty sure I understand it. The issue is that I'm currently trying to estimate a model with a 14-level "nusiance" factor as an independent variable...which makes the output quite ugly. All I'm really interested in is the question of whether these factor as a whole

Daniel, When Group is entered as a factor, and the factor has two levels, the ANOVA table gives a p value for each level of the factor. What I am looking for is the omnibus p value for the factor, i.e. the test that the factor (with all its levels) improves the prediction of the outcome. You are correct that normally one could rely on the fact that the model

Juraj17 wrote: > > Do I have to write my own, or it exists yet? How name has it, or how can I > use it. > Try the R-function search. It return the function you are looking for as the first match. Dieter -- View this message in context: http://r.789695.n4.nabble.com/D-Agostino-test-tp3424952p3425833.html Sent from the R help mailing list archive at Nabble.com.

When I try to load "fBasics" package, I get following error/warning : > library(fBasics) Loading required package: fImport Loading required package: fSeries Loading required package: fBasics Loading required package: fImport Loading required package: fSeries Loading required package: fBasics Loading required package: fImport Loading required package: fSeries Loading required package:

I have a question about the D'Agostino skewness test and the Anscombe-Glynn kurtosis test. agostino.test(x, alternative = c("two.sided", "less", "greater")) anscombe.test(x, alternative = c("two.sided", "less", "greater")) The option "alternative" in those two functions seems to be the null hypothesis. In the output, the

Prof Brian Ripley <ripley at stats.ox.ac.uk> wrote: mkdir fBasics unzip fBasics.zip -d fBasics rm fBasics/src/*.o R CMD check fBasics and that took me about 3 minutes. Now me, I just did unzip -a fBasics_190.10051.zip R CMD INSTALL fBasics rm -rf fBasics in a naive and trusting manner. It took me considerably longer than 3 minutes to learn that this was what I should do, and

Hi R-users, I would like to detach and installed the fBasics package but it gives me this message: >     detach("package:fBasics",unload=TRUE) Error in detach("package:fBasics", unload = TRUE) : invalid name >     install.packages(repos=NULL,pkgs="c:\\Tinn-R\\fBasics_2100.77.zip") package 'fBasics' successfully unpacked and MD5 sums checked Warning:

Greetings, I have generated several ROC curves and would like to compare the AUCs. The data are cross sectional and the outcomes are binary. I am testing which of several models provide the best discrimination. Would it be most appropriate to report AUC with 95% CI's? I have been looking in to the "net reclassification improvement" (see below for reference) but thus far I can only

Hello I do not know very much about statistics (and English language too :-( ), then I come in search of a clarification (explanation): I found two distinct results on KURTOSIS and I do not know which of them is the correct one. Any aid will be welcome! klebyn ################ CODE rnorm(1000) -> x library(moments) kurtosis(x) skewness(x) detach("package:moments")

Hi I'm trying to write a generic script for processing some data which finishes off with some plots. Given Im never sure how many columns will be in my dataframe I wanted to using the following plot(spectra.wavelength, cormat, type = "l", ylim=c(-1,1), xlab="Wavelength (nm)", ylab="Correlation") however even if I specify as type="l" it appears plot

Hi, I'm trying to match peaks between chromatographic runs. I'm able to match peaks when they are chromatographed with the same method, but not when there are different methods are used and spectra comes in to play. While searching I found the ALS package which should be usefull for my application, but I couldn't figure it out. I made some dummy chroms with R, which mimic my actual