similar to: dixon test

I would like to extend Dixon's values beyond 30. I've read over Rorabacher article but didn't understand the equations well enough to convert them to Excel and then "drag" the cells out extending the n. Rorabacher,_1991.pdf <http://r.789695.n4.nabble.com/file/n4649819/Rorabacher%2C_1991.pdf> Dean_&_Dixon,_1951.pdf

I have been learning about some outlier tests -- Dixon and Grubb, specifically -- for small data sets. When I try help.start() and search for outlier tests, the only response I manage to find is the Bonferroni test avaiable from the CAR package... are there any other packages the offer outlier tests? Are the Dixon and Grubb tests "good" for small samples or are others more

I am using R dixon.test in order to perform Q test for detection of outliers. The p-values for high Q values are consistent with the table in Rorabacher, D.B. (1991) http://www.flworkshop.com/sscs/dixon_test.pdf, but it seems that small Q-values get problematic p-values. For example: dixon.test(c(0.324,0.5,1,1.324),two.sided=T) Q = 0.324, p-value = 1

Hi all, please, someone can explain me how update my R version? thank you! giov -- View this message in context: http://www.nabble.com/R-update-tp19291451p19291451.html Sent from the R help mailing list archive at Nabble.com.

Hi, I have little experience using wavelet and I would like to know if it is possible,using R wavelet package, to have a plot of frequency versus time. thank you giov -- View this message in context: http://www.nabble.com/help-on-wavelet-tp19395583p19395583.html Sent from the R help mailing list archive at Nabble.com.

Hi all, I used the function jarque.test (in the moments package) on my data set and I obtained something like this: Jarque-Bera Normality Test data: x JB = 4.8381, p-value = 0.089 alternative hypothesis: greater or Jarque-Bera Normality Test data: x JB = 2.6018, p-value = 0.2723 alternative hypothesis: greater I cannot understand this. Please, someone can help me? thank you

Hello, everyone, I want to ask a simple question. If I have a set of data,and I want to identify how many outliers there are in the data.Which packages and functions can I use? Thanks. Shao chunxuan. [[alternative HTML version deleted]]

hello, are there functions to detecte outlying observations in samples? thanks. ___________________________________________________________________________ [[alternative HTML version deleted]]

Dear R-users, Has anyone written a function for multifractal detrended fluctuation analysis? The "fractal" package does mono-fractal DFA, but not multifractal as far as I can tell. The MF-DFA approach is presented in: J. W. Kantelhardt, S. Zschiegner, E. Koscielny-Bunde, S. Havlin, A. Bunde, and H. E. Stanley, "Multifractal Detrended Fluctuation Analysis of

I would like to do some fairly basic stem-and-leaf displays in R. I am aware (I might even say painfully aware) of stem(base) and have tried it. That's why I'm hoping someone has a usable stem- and-leaf display for R so that I don't have to write my own. r-project.org > Search > R Site Search > "stem and leaf display" finds nothing. I also tried the mail archive

Hi, I am currently analysis Raman spectroscopic data with the hyperSpec package. I consulted the documentation on this package and I found an example work-flow dedicated to Raman spectroscopy (see the address : http://hyperspec.r-forge.r-project.org/chondro.pdf) I am currently trying to remove outliers thanks to PCA just as they did in the documentation, but I get a message error I can't

Hi Rers, W/o trying to make a long story short (I hope you read that correctly), I have been working on a nice little R function ... Somewhere along the line I got an error that refers to a max function in my function (I think this is a vector vs. scalar issue): Error in max(..., na.rm = na.rm) : invalid "mode" of argument If that's not bad enough, even when I q() R and say

I have a statistical question. The data sets I am working with are right-skewed so I have been plotting the log transformations of my data. I am using a Grubbs Test to detect outliers in the data, but I get different outcomes depending on whether I run the test on the original data or the log(data). Here is one of the problematic sets: fgf2p50=c(1.563,2.161,2.529,2.726,2.442,5.047)

Suppose I am reading data from a file and the data contains some outliers. I want to know if it is possible in R to automatically detect outliers in a dataset and remove them -- View this message in context: http://n4.nabble.com/How-to-detect-and-exclude-outliers-in-R-tp1017285p1017285.html Sent from the R help mailing list archive at Nabble.com.

Hi All, I am working on the dataset in which some of the variables have more than one observations with outliers . I am using below mentioned sample script library(outliers) x1 <- c(10, 10, 11, 12, 13, 14, 14, 10, 11, 13, 12, 13, 10, 19, 18, 17, 10099, 10099, 10098) outlier_tf1 = outlier(x1,logical=TRUE) find_outlier1 = which(outlier_tf1==TRUE, arr.ind=TRUE) beh_input_ro1 =

I have a SGI Altix 350 (ia64) that came with RHAS 4, but without any paid support. I would like to migrate the server over to CentOS. Here is what I've pieced together from some older posts by Johnny, can someone confirm this should work? I know this is somewhat covered in the FAQ, although both entries refer to FC or WBEL, which already have built-in yum support. Install the

Hi, this is both a statistical and a R question... what would the best way / test to detect an outlier value among a series of 10 to 30 values ? for instance if we have the following dataset: 10,11,12,15,20,22,25,30,500 I d like to have a way to identify the last data as an outlier (only one direction). One way would be to calculate abs(mean - median) and if elevated (to what extent ?) delete the

Hello: I found the "hubers" function in MASS library is NOT working on the following data: > a <-

I'm trying to get dovecot working with various clients on my new mailserver. I installed the base rc2 that comes with OpenBSD 4.0, then upgraded to rc14 when I ran into problems with TLS and Thunderbird. The server has no problems with Mail.app on my laptop, but any connection attempts from Thunderbird timeout after the TCP handshake. 13:53:41.074438 66.x.x.2.50483 >

Happy holidays all! I know it's very subjective to determine whether some data is outlier or not... But are there reasonally good and realistic methods of identifying outliers in R? Thanks a lot! [[alternative HTML version deleted]]