similar to: Displaying "homogeneous groups" in aov post-hoc results ?

Hello, Using the dataset below I produce a soil.texture plot (R code for this included at the end). One will notice I have plotted the points based on whether or not they are in a region called 'upstream' or 'downstream'. I'm curious if there is a way to somehow extract counts of the number of points (red points and blue points) falling within each classification (e.g. silty

Does an array of a value type and a structure containing only that type have the same alignment requirements?  That is, in C-syntax: float* array; struct point { float x; float y; } point p; array = (float*)&p; Does `array[0]` now refer to x, and `array[1]` to y? I'm creating these structures in LLVM with default packing (StructType::isPacked == false). I have a library that takes

On Fri, 2 Sep 2017, Don via samba wrote: > Greetings, > > For sake of failover it's time for me to add a second samba DC to a > domain with a single samba DC. At present, there's only one samba DC. > There are no Windows DCs. > > The documentation seems a little dated: > > https://wiki.samba.org/index.php/Samba_4.0_Whitepaper Try the following instead:

I want to do a non homogeneous poisson process model in R. Any advice, or know of places where i can get some, ive googled it but nothing came up relating to R. -- View this message in context: http://old.nabble.com/non-homogeneous-poisson-process-tp26378037p26378037.html Sent from the R help mailing list archive at Nabble.com.

Hello, I'd like to upstream our work over the time which the community would benefit from. This is a part of effort toward minimizing code size presented in here <https://llvm.org/devmtg/2020-02-23/slides/Kyungwoo-GlobalMachineOutlinerForThinLTO.pdf>. In particular, this RFC is about optimizing prolog and epilog for size. *Homogeneous Prolog and Epilog for Size Optimization, D76570

Hi Vedant, Thanks for your interest and comment. Size-optimization improves page-faults and a start-up time for a large application, which this enabling also followed. Even though I didn't see a large regression/complaint on a CPU-bound case, which is not a typical case for mobile workload, I wanted to be precautious of enabling it by default. However, as with default outlining case, I

##I want to remove the rows where the row sums are zero and this is as far as I have gotten ffg <- (structure(list(CD = c(0, 0, 0, 0, 3.125, 0, 0, 0, 0, 1.6, 3.125, 0, 0, 6.25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.125, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.125, 0, 0, 0, 0, 0, 0, 0, 0,

I have a KVM vm running CentOS-6.8 on a host also running CentOS-6.8. This instance is used for occasional development projects which require segregation. Thus it is seldom accessed. At some point in the recent past this guest developed an issue with starting. Specifically these messages were found in the system log files: /var/log/messages-20170312:Mar 10 16:31:06 vhost04 kernel: dracut:

Hi List. It may be a very simple question, but I couldn’t find an answer on the internet. Which function (maybe in a specific package) would I use to perform a Cochran C Test for Homogeneity of Variances? Stats package have the mantelhaen.test, but I’m “almost sure” that It’s not what I want, it’s probably the Cochran Q Test. So, any ideas? Thank you for the pacience.

Dear r-helpers, Prior to analysis of variance, I ran the Boxcox function (MASS library) to find the best power transformation of my data. However, reading the Boxcox help file, I cannot figure out if this function (through its associated log-likelihood function) corrects for * normality only * or if it also induces * homogeneity of variances *. I found in Biometry (Sokal and Rohlf, p. 419)

Hi, All! To calculate Levene's Test for Homogeneity of Variance I use R Commander, and this is the output: > levene.test(Dataset$age, Dataset$sex) Levene's Test for Homogeneity of Variance Df F value Pr(>F) group 1 0.8739 0.3567 33 I am not sure what means "Pr(>F)"? Can anyone explain/translate this? Regards, Iurie Malai Department of Psychology and

Dear R-users, We expect to create N homogenic groups of n features from an experimentation including N*n mesures. The aim of this is to prevent from group effects. How to do that with R functionalities. Does anyone know any methodes enabling this ? Best regards. Alexandre MENICACCI Bioinformatics - FOURNIER PHARMA 50, rue de Dijon - 21121 Daix - FRANCE a.menicacci at fr.fournierpharma.com t??l

I partly answered my question since independence_test() function in coin package apparently do Cochran-Armitage trend test just like Eric Lecoutre's function tabletrend() - slightly modified here: > independence_test(pheno ~ geno, data = dat2, teststat = "quad", scores = list(geno = c(0, 1, 2))) Asymptotic General Independence Test data: pheno by groups 1 < 2

On Wednesday 04 Apr 2012 13:27:07 Tim Northover wrote: > Putting that information in the InputArg/OutputArg and incorporating it the > CCAssignFn interface allows a more straightforward implementation in the > targets, in my view (for both our uses). It's also information that's > readily available when InputArg/OutputArgs are being constructed. In your > case: > >

Dear Group Members, Forgive me if I am a little bit out of subject. I am looking for a good way to test the homogeneity of two variance-covariance matrices using R, prior to a Hotelling T test. Youll probably tell me that it is better to use a robust version of T, but I have no precise idea of the statistical behaviour of my variables, because they are parameters from the harmonics of

Dear all, is there any possibility to generate a table of homogeneous subsets for multiple comparison tests as in SPSS with R? I am specially interested in Tukey's B test. I have tried to find the answer myself and found an excellent description here: http://www.ppsw.rug.nl/~sda/siv/Posthoc_print.pdf (-> Tab. 2) Together with the information here ...

I am testing the homogeneity of variances via bartlett.test and fligner.test. Using the following example, how should I interpret the p-value in order to accept or reject the null hypothesis ? set.seed(5) x <- rnorm(20) bartlett.test(x, rep(1:5, each=4)) Bartlett test of homogeneity of variances data: x and rep(1:5, each = 4) Bartlett's K-squared = 1.7709, df = 4, p-value =

Hello people, I am in need for testing Marginal Homogeneity for sparse (more then 2X2) matrixes. After searching, what I found by now is that for more then 2 by 2 matrixes, one turns to "stuart maxwell tests" that are available in two packages: irr - see: stuart.maxwell.mh coin - see: mh_test But I couldn't find in the documentation how valid the results are for sparse matrixes,

Dear R-users, We expect to form N homogeneous groups of n features from an experimentation including N*n data. The aim is to prevent group effects. How to do that with R functionalitites ? Does anyone know any way enabling this ? Example : 100 patients are observed. 3 biochemical parameters are mesured for each one (Red and white globules ans glycemia). Patient RG RW

Hello, Sorry if it's a simple question, this is my first script complex, but can not find solution in the list I have many TXT files with daily data of meteorological stations and a table that connects it with each of these stations. (More than one table per station and date). With the Script attempt: º1 Fit the data and create a regular series from 01.01.2000 to 12.31.2010 data for all