similar to: : unusual combinations of categorical data

I am out of the office until 01/05/2012. Please note that I have changed email address. You can contact me at alan.chalk@uk.rsagroup.com Note: This is an automated response to your message "R-help Digest, Vol 100, Issue 26 [Virus checked]" sent on 25/06/2011 11:00:11. This is the only notification you will receive while this person is away. Notice of Confidentiality This

I get the following log messages using rsync 2.5.1 compiled on Solaris 8 when using the "hosts allow" option in rsyncd.conf: 2002/01/23 12:09:51 [28276] rsync: reverse name lookup mismatch on fd0 - spoofed address? 2002/01/23 12:10:18 [28276] rsync: connection unexpectedly closed (16 bytes read so far) Resolv.conf/DNS are configured and working properly with both A and PTR records

To whom it may concern, Would appreciate it if anyone knows how we might easily exclude NFS mount points within a file system from being rsync'd? Regards, Craig Donnelly Andel Consulting 10 Fenchurch Avenue London EC3M 5BN Tel: 0207 6655055 Fax: 0207 6655060

Dear all, I'm trying to produce a log-likelihood profile for a GPD estimate with the ismev package. When following the examples, everything is working fine, i.e. I get a nice parabolic log-likelihood curve. When using my own data (intensity of hurricanes at landfall), the log-likelihood curve looks like a step function. Am I doing something wrong here? Is my data not suited for the GPD fit?

I am a Professor of Statistics at Indira Gandhi Krishi Vishwavidyalaya, Raipur, India. While teaching in class about analysis of variance using R, I was doing a one-way analysis for the two data-sets given below in the R-class. I got a typical error in "emmeans" package, please help: Data-set-1: -------------- Medley and Clements (1998) investigated the impact of zinc contamination (and

I have been trying to write a function for the following problem: Suppose I have three vectors a,b,c of different lengths: e.g. a=c(a1,a2,a3,...) where a[i] form the basis of our function variables: if we define a table for example: and define the fn(x) <-function{..sum(argument)..} where x<-c(a,b,c) so that we can maximise fn(x) as:

On 07/01/18 02:19, Akhilesh Singh wrote: > I am a Professor of Statistics at Indira Gandhi Krishi Vishwavidyalaya, > Raipur, India. While teaching in class about analysis of variance using R, > I was doing a one-way analysis for the two data-sets given below in the > R-class. I got a typical error in "emmeans" package, please help: > > Data-set-1: > --------------

I am out of the office until 30/11/2012. Please send work related emails to laura.jordan at uk.rsagroup.com or personal emails to alanchalk at gmail.com. Note: This is an automated response to your message "R-help Digest, Vol 117, Issue 3" sent on 03/11/2012 11:00:07. This is the only notification you will receive while this person is away. Please consider the environment - Think

Thanks for your kind reply. Problem is solved. However, it's "confidence interval / treatment comparison plot" is not taking main title. And the fonts of axes labels can not be changed using 'cex' parameter. I will appreciate if you could help in this matter too. Dr. A. K. Singh On 09-Jan-2018 8:18 PM, "Sal Mangiafico" <salvatore.s.mangiafico at gmail.com>

Hai James, So a windows xp works but Win7/10 not, at least is good hint. So, i did have a look in my setup again. And i'm thinking, i have disabled ipv6 for my windows 7 and win 10 pc's. That may be an option.. A thing you can try, have a look here : http://www.bvanleeuwen.nl/faq/?p=1142 ipv6 Admx to simple disable ipv6. I've set : Disable all IPv6 components. I also

Dear R-users, I have two outliers related questions. I. I have a vector consisting of 69 values. mean = 0.00086 SD = 0.02152 The shape of EDA graphics (boxplots, density plots) is heavily distorted due to outliers. How to define the interval for outliers exception? Is <2SD - mean + 2SD> interval a correct approach? Or should I define 95% (or 99%) limit of agreement for data interval,

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, I need some help using the R outliers package. I would like to perform a Q-test (Dixon test) on my data set. I used the dixon.test function, but I cannot understand what is the confidence level used to perform the test. I have n=101 (n= number of data). So, can I use directly dixon.test ? What about qdixon and qtable functions? thank you so much! -- View this message in context:

hi, I tried to build libshout 2.2.2 in my pc using cygwin. /.configure produce the messages below. Anyone knows how to fix it. Thanks /james --------------------------------------- checking winsock2.h usability... no checking winsock2.h presence... yes configure: WARNING: winsock2.h: present but cannot be compiled configure: WARNING: winsock2.h: check for missing prerequisite headers?

Hello, I've been searching for a method for identify outliers for quite some time now. The complication is that I cannot assume that my data is normally distributed nor symmetrical (i.e. some distributions might have one longer tail) so I have not been able to find any good tests. The Walsh's Test (http://www.statistics4u.info/ fundsta...liertest.html#), as I understand assumes that the

Estimados colegas: Entiendo que este buzón es el adecuado (me disculpan si no es así) para dar a conocer el siguiente paquete de R: *MUOD (outliers)* luisfo/muod.outliers | | | luisfo/muod.outliers | El paquete, tal y como se indica, está respaldado por un paper que hemos publicado recientemente en Scientific Reports. Detecta outliers en datos multidimensionales usando 'function

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 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)

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]]

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