similar to: Scale time series in a way that 90% of the data is in the -0.-9/ +0.9 range

I am having trouble plotting outliers on time series. Give then following code: ############################################################ # find STL Outliers by weight and append sh2, use Robust # this should allow the initial outliers to be filtered # this section may be commented out. ############################################################ tsSourceDiag <-

Hello, I am constructing a boxplot but have a very wide range of values (zero - ~28000). I have placed an axis break at the 8000 mark but would like to have a different scale above the break that ranges from 8000-28000. Right now my axis is so large that the boxplots are only represented as lines with the outliers above. Does anyone know where I could find code that explains how to do this?

Hello, I need to analyse a data matrix with dimensions of 30x100. Before analysing the data there is, however, a need to remove outliers from the data. I read quite a lot about outlier removal already and I think the most common technique for that seems to be Principal Component Analysis (PCA). However, I think that these technqiue is quite subjective. When is an outlier an outlier? I uploaded

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)

For the analysis of financial data wih a large variance, what is the best way to select an outlier threshold? Listed below, is there a best method to select an outlier threshold and how does R calculate it? In R, how do you find the outlier threshold through an interquartile range? In R, how do you find the outlier threshold using the hist command? In R, how do you find the outlier threshold

Dear List and Hadley, I would like to have a boxplot with ggplot2 and have the outlier values labelled with their "name" attribute. So I did > library(ggplot2) > dat=data.frame(num=rep(1,20), val=c(runif(18),3,3.5), name=letters[1:20]) > p=ggplot(dat, aes(y=val, x=num))+geom_boxplot(outlier.size=4, outlier.colour="green") >

Hello, Writing to seek help in regard to some unexpected performance anomaly i am observing in using tsoutlers:tso on the mac vs on an AWS cloud server.. I am running the following code with very small dataset of about 208 records. d.dir <- '/Users/darshanpandya/xxxxxx' FNAME <- 'my_data.csv' d.input <- fread(file.path(paste0(d.dir,"/zzz/"),FNAME,fsep =

Last week there was a thread on outlier detection. I came across an article which has a very interesting paragraph. The article is Missing Values, Outliers, Robust Statistics, & Non-parametric Methods by Shaun Burke, RHM Techology Ltd, High Wycombe, Buckinghamshire, UK. It was the fourth article in a series which appeared in Scientific Data Management in 1998 and 1998. The very

Hello all, Let me first say that this isn't a question about outliers. I am using the outlier function from the outliers package but I am using it only because it is a convenient wrapper to determine values that have the largest difference between itself and the sample mean. Where I am running into problems is that I am several groups where I want to calculate the "outlier" within

Hello all, I wrote a small function to add labels for outliers in a boxplot. This function will only work on a simple boxplot/formula command (e.g: something like boxplot(y~x)). Code + example follows in this e-mail. I'd be happy for any suggestions on how to improve this code, for example: - Handle boxplot.matrix (which shouldn't be too hard to do) - Handle cases of complex

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

Hola Marta, si observas cualquier artículo de psicología esto es una práctica típica. Te digo psicología porque creo que tus datos son de ese tipo. Tienes bibiligrafía de artículos cientificos en las que se quitan valores siguiendo los criterios que te he dicho solo has de buscar bibliografía del campo en el que te mueces. Osea que nos quitarlos por que sí, aunque en cada area de ciencia tienes

Hello, I'm seeking ideas on how to remove outliers from a non-normal distribution predictor variable. We wish to reset points deemed outliers to a truncated value that is less extreme. (I've seen many posts requesting outlier removal systems. It seems like most of the replies center around "why do you want to remove them", "you shouldn't remove them", "it

Hello, Writing to seek help in regard to some unexpected performance anomaly i am observing in using tsoutlers:tso on the mac vs on an AWS cloud server.. I am running the following code with very small dataset of about 208 records. d.dir <- '/Users/darshanpandya/xxxxxx' FNAME <- 'my_data.csv' d.input <- fread(file.path(paste0(d.dir,"/zzz/"),FNAME,fsep =

hi - if I sample from a normal distribution with something like n100<-rnorm(100,0,1) and add an outlier with n100[10]<-4 then qqnorm(n100) visually shows the point 4 as an outlier and calculating the probablity of a value of 4 or bigger in 100 samples of norm(0,1) gives > 1-exp(log(pnorm(4,0,1))*100) [1] 0.003162164 If I have more than 1 sample above outlier threshold the math is a

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

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 =

This is my a part of my data set > D[1:15,c(1,5:10)] X. media IE.2005 IE.2006 IE.2007 IE.2008 IE.2009 IE.2010 1 1108 22.00000 60.0 39 4.0 8.0 16.0 5.0 2 1479 110.00000 NA NA 53.0 1166.0 344.8 110.0 3 1591 86.60000 247.0 87 95.0 94.0 81.0 76.0 4 3408 807.00000 302.0 322 621.0 1071.0 1301.0 1225.0

Disclaimer/Apology: I am an R newbie I am seeing some behaviour that seems to me to be the result of some cacheing going on at some level, and perhaps this is expected behaviour. I would just like to understand the basic rules. What I have is a file with some data. I read it in and then do a summary on the resulting dataframe. I find the some values are completely outside the expected range,