similar to: Drop values of one dataframe based on the value of another

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)

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

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.

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

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

Tu fichero tiene los decimales como puntos y no como comas como tu le indicas. Te dejo un ejemplo #--------------------------------------------------------------------------------------------------------------------- setwd(dir="c:/Users/usuario/Desktop/") library(outliers) filename<-"timediff.csv" time<-read.csv(filename, sep=";",header=TRUE,dec=".")

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,

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 new to R and I would like to get your help in finding 'outliers'. I have mvoutlier package installed in my system and added the package . But I not able find a function from 'mvoutlier' package which will identify 'outliers'. This is the sample list of data I have got which has one out-lier. 11489 11008 11873 80000000 9558 8645 8024 8371 It will

dear all, could somebody tell me how I can plot mild outliers as a circle(?) and extreme outliers as an asterisk(*) in a box-whisker plot? Thanks very much in advance -- View this message in context: http://www.nabble.com/mild-and-extreme-outliers-in-boxplot-tp25040545p25040545.html Sent from the R help mailing list archive at Nabble.com.

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

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, 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

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

Hi All- I have 24 boxplots on one graph. I do not have the whiskers extending to the outliers, but I would like to label the maximum value of each outlier above the whiskers. I have the stats but am having trouble figuring out how to label the whiskers. Any suggestions would be great! sherri

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 dear all, i have a data (data.frame) which contain y and x coloumn(i.e. y x 1 0.58545723 0.15113102 2 0.02769361 -0.02172165 3 1.00927527 -1.80072610 4 0.56504053 -1.12236685 5 0.58332337 -1.24263981 6 -1.70257274 0.46238255 7 -0.88501561 0.89484429 8 1.14466282 0.34193875 9 0.58827457 0.15923694 10 -0.79532232 -1.44193770 ) i changed

Hello all, I have a question: I am using the interquantile method to spot outliers & it gives me values of say 234 & -120 or for the higher & lower benchmarks. I don't have any issues w/ the higher end. However I don't have any negative values. My lowest possible value is 0. Should I consider 0 as an outlier? Thanks ahead for your thoughts -- View this message in

I am working on a function that will remove outliers for regression analysis. I am stating that a data point is an outlier if its studentized residual is above or below 3 and -3, respectively. The code below is what i have thus far for the function x = c(1:20) y = c(1,3,4,2,5,6,18,8,10,8,11,13,14,14,15,85,17,19,19,20) data1 = data.frame(x,y) rm.outliers =