similar to: Outlier colors in image()

Hi, all. I made the main figure of levelplot upside down by transposing and proper indexing; upper left corner is the biginning of matrix. However, I can't figure out how I can upside down the vertical colorkey right side of the main figure. Any help will be greatly appreciated. Minho

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

Currently I find that if I call stl() repeatedly I can use the weights array that is part of the stil output to detect outliers. I also find that if I repeatedly call stl() (replacing the outliers after each call) that the "remainder" portion of the stil output gets reduced. I am calling it like: for(.index in 1:4) { st <- stl(mt, s.window=frequency(mt), robust=TRUE)

I'm trying to use a paper by Hardin & Rocke: http://handel.cipic.ucdavis.edu/~dmrocke/Robdist5.pdf as a guide for a function to identify outliers in multivariate data. Attached below is a function that is my attempt to reproduce their method and also a test to see what fraction of the data are identified as outliers. Using this function I am able to reproduce their results regarding the

Hi there, I'd like to create a heatmap from my matrix with a) a defined color range (lets say from yellow to red) b) using striking colors above and below a certain threshold (above = "green", below = "blue") Example matrix (there should be a few outliers generated...) + simple levelplot without outliers marked: library(lattice) my.mat <- matrix(rnorm(800), nrow =

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)

I am a new user of R so please bear with me. I have reviewed some R books, FAQs and such but the volume of material is great. I am in the process of porting my current SAS and SVS Script code to Lotus Approach, R and WordPerfect. My question is, can you help me determine the best R method to implement the Hoaglin Outlier Method? It is used in the Appendix A and B of the fo llowing link.

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, I was just trying to figure out how to beautify the output of my bwplot-output. Altogether I figured most of the things out on my own. The one thing which puzzles me though are the symbols for the outliers. I can easily change the form of the median symbol by using "pch" but I don't know how to do this for outliers. Obviously the "outpch" of the

I try to use ?randomForest to find variables that are the most important to divide my dataset (continuous, categorical variables) in two given groups. But when I plot the outliers: plot(outlier(FemMalSex_NAavoid88.rf33, cls=FemMalSex_NAavoid88$Sex), type="h",col=c("red","green")[as.numeric(FemMalSex_NAavoid88$Sex)]) it seems to me that all my values appear as

I have a large set of data from an experiment in which each individual has trials 1-40 with approx. 150 points sampled every 0.1 sec within each trial. Each trial is a column and the rows are organized by time.  I know there is a code to take out single outliers within a certain trial by using the chi square test code. But, I want to be able to determine if trials are outliers. I am having a tough

Dear R users, I am tryting to save an 3d array to a matlab file like the following. A <- array(1:24, c(2,3,4)) writeMat(filename, A=A) But if I load the mat file from Matlab, it is not 3d matrix anymore. Does anyone know how I can preserve the 3d structure? Any help will be greatly appreciated. Minho Chae [[alternative HTML version deleted]]

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 need to detect outliers in a large data set which is highly right-skewed. I plan to use medcouple-based outlier detection. Is there any support for medcouple-based outlier detection in R? Are there any other routines in R to perform outlier detection in highly right-skewed data? Manuj Sharma See the Web&#39;s breaking stories, chosen by people like you. Check out Yahoo! Buzz.

Hi, I was looking for a package that would help with outlier detection for bimodal distributions. I have tried 'outliers' and 'extremevalues' packages, but am not sure if they are ok for bimodal distribution. Any help would be highly appreciated! thanks, [[alternative HTML version deleted]]

Dear R-experts, Searching the help archives I found a recommendation to do multivariate outlier identification by mahalanobis distances based on a robustly estimated covariance matrix and compare the resulting distances to a chi^2-distribution with p (number of your variables) degrees of freedom. I understand that compared to euclidean distances this has the advantage of being scale-invariant.

I have distribution that are gaussian to a good approximation. I fit a gaussian to these distributons. Once in a while there is an outlier. Could someone suggest a robust method (R package already?) that removes those outliers and redoes the gaussian fit to get a better fit? Thanks. [[alternative HTML version deleted]]

I have the following data as input, from which I would like to make a lattice levelplot: ----------------- x y level 1 m3134 m3134 1.0000000 2 m3134 m416B 0.4189057 3 m416B m3134 0.2696508 4 m3134 mA20 0.3322170 5 mA20 m3134 0.2454191 6 m3134 mB 0.3176792 ... ----------------- I transform the levels as follows, so that

Hi, I want to calculate the R-squared between two variables. Can you advice me how to identify and remove the outliers before performing R-squared calculation? Thanks, Kan --------------------------------- SBC Yahoo! DSL - Now only $29.95 per month! [[alternate HTML version deleted]]

Hello, Which outlier detection test is robust for large dataset ? I think the detection test in the package 'outliers' are suitable for small data set (between 3 and 100). Do you know one for 20000 or more values ? Thanks, - Martial _________________________________________________________________ Inédit ! Des Emoticônes Déjantées! Installez les dans votre Messenger ! [[alternative