similar to: selecting a coordinate from a graph

Hi, I'm using gl1ce with family=binomial like so: >yy succ fail [1,] 76 23 [2,] 32 67 [3,] 56 43 ... [24,] 81 18 >xx c1219 c643 X1 0.04545455 0.64274145 X2 0.17723669 0.90392792 ... X24 0.80629054 0.12239320 >test.gl1ce <- gl1ce(yy ~ xx, family = binomial(link=logit), bound = 0.5 ) or >omit <- c(2,3) >test.gl1ce

Hi everybody, I'd like to know if there's an easy way for extracting outliers record from a dataset, in order to perform further analysis on them. Thanks Alessandro

Dear List, I'd like to make boxplots of a large number of observations (+/- 20.000), which are distributed log-normal and right skewed. The problem is that with standard boxplots a too large number of observations are displayed as outliers. I also tried to display the log of the observations, but even then there are to may outliers to my taste. So I'd like to change the standard IQR box

I have the following procedure which worked just fine for in R 2.2.0. Recently I upgraded to 2.6.1 and now get an error: > ScatterOutlier(pass_500_506[1:1000,6:12], marginal_500_506[,6:12]) Error in eval(expr, envir, enclos) : object "out" not found Note that I use the same workspace (and hence data) as in 2.2.0. When I make sure that the object "out" exists at

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

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 =

Hello- I have a script which steps through a series of subjects, and for the subjects I remove outlying values. After removing these outliers, I specify a cutoff, keeping only values over a certain value (e.g., 1.96). I want to populate a matrix with a statistic of the values that make the cutoff (for example, the mean). However, in some subjects, after outliers and the cutoff are specified,

Hello, I generate box plots from my data like this: qplot(x=xxx,y=column,data=data,geom="boxplot") + xlab("xxx") + ylab(ylabel) + theme_bw() + scale_y_log10() + geom_jitter(alpha=I(1/10)) The problem is that I see lot of points above the maximum at the same level as some outliers. It looks very weird as I expected the outliers to be "few" and specially not see any

Dear list Getting very frustrated with this simple-looking problem > m1 <- lm(x~y, data=mydata) > outliers <- abs(stdres(m1))>2 > plot(x~y, data=mydata) I would like to plot a simple x,y scatter plot with labels giving custom information displayed for the outliers only, i.e. I would like to define a column mydata$labels for the mydata dataframe so that the command >

Thanks for writing this great piece of code. x = rnorm(100) boxplot(x) # you shouldn't see any outliers here although sometimes yow will # lets add some outliers intentionally x = c(21, 20, 25, x) # now 10, 15 and 20 are outliers myboxplot <- boxplot(x) # now you should see your three outliers myboxplot$out # it will print the values of the outliers How does one amend

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 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

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

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.

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,

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

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.

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 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 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 =