similar to: Pierce's criterion

Hello, I'm new in this R world and I don't know much about statistics, but now I have to analize some data and I've got some first queries yet: I have 5 sets of area mesures and each set has 5 repetitions. My first step is to check data looking for outliers. I've used the outliers package. I have to use the cochran test and the grubbs test in case I find any outlier. The problem

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)

Hi! I'm trying to implement an outlier test once/row in a large dataframe. Ideally, I'd do this then add the Pvalue results and the number flagged as an outlier as two new separate columns to the dataframe. Grubbs outlier test requires a vector and I'm confused how to make each row of my dataframe a vector, followed by doing a Grubbs test for each row containing the vector of numbers

Dear All, I have small samples of data (between 6 and 15) for numerious time series points. I am assuming the data for each time point is normally distributed. The problem is that the data arrvies sporadically and I would like to detect the number of outliers after I have six data points for any time period. Essentially, I would like to detect the number of outliers when I have 6 data points then

Dear all, I am unsure about how to specify a model in R and I thought of asking some advice to the list. I have two groups ("Group"= A, B) of subjects, with each subject undertaking a test before and after a certain treatment ("Time"= pre, post). Additionally, I want to enter the age of the subject as a covariate (the performance on the test is affected by age),

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]]

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]]

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

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

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I'm in the process of learning Samba and evaluating it as a possible replacement for Windows NT servers in our office next year. I have set up a small test network comprised of one NT 4 (SP6a) Workstation, one Windows 2000 Pro (SP3) and one Fedora Core 3 running samba 3.0.11 as a PDC. I'm using LDAP as the passdb backend with smbldap-tools 0.8.7-1. The Windows 2000 Pro machine can join the

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

Chapman & Hall/CRC: The R Series We are delighted to announce that our new series of books on R is up and running, with two books already published and another nine forthcoming (including three set to publish in 2012). We are keen to receive proposals for books covering all aspects of the development and application of R software. If you have an idea for a book, please contact one of the

Hi, I did not find any web page about using R in economics and econometrics so far. However, this does not mean that there is none (searching with google for "R" and "economics" gives many pages about economics and a name like Firstname R. Lastname on it ;-)). Does anybody in the list does know such a web page? If not, I will be happy if you, Ajay, could build and

Hi, The department of economics at our university (Budapest) is planning a course on numerical methods in economics. They are trying to decide which software to use for that, and I would like to advocate R. The other alternative is Matlab. I have found comparisons in terms of computational time for matrix algebra, but I don't think that is relevant: the bottleneck for economists is usually

How for the love of god can I prevent the lm() function from padding on to my factor variables? I start out with 2 tables: Table1 123123 124351 ... 626773 Table2 Count,IS_DEAD,IS_BURNING 1231,T,F 4521,F,T ... 3321,T,T Everything looks fine when I import the data. then we get a oh_crap <- lm(table1 ~ Count + IS_DEAD + IS_BURNING, table2) Magically when I look at my oh_crap coefficents

Cologne, 10.12.00 Dear Sir, Dear Madam, We are concerned about the possibility that the European Commission might introduce software patenting into the European Community because we think this will harm our profession. We make our living on data management and statistical analyses. Modern statistics crucially depends on algorithms [cf. Venables, W. N., & Ripley, B. D. (1999). Modern applied

Dear Professor Bernhard, Sorry for take your time, but I found something strange that I am not able to explain/understand. Suppose that I compute the ADF test by using the criterion="BIC" to select the lags: summary(CADFtest(y, max.lag.y = 20, type = "drift", criterion="BIC")) Suppose that 2 lags are selected. Next, if I set the lags to 2: summary(CADFtest(y,

Hi, I am a new user of R. This is a conceptual doubt regarding screeing out outliers from the dataset in regression. I read up that Cook's distance can be used and if we want to remove influential observations, we can use the metric (>4/n) (n=no of observations) to remove any outliers. I also came across Grubb's test to identify outliers in univariate distns. (assumed normal) but i