similar to: transformation and outliers

Dear all I wonder if anyone has heard of confidence intervals around p-values... Any pointer would be highly appreciated. Best Fer

Dear list, How can I add the mean and standard deviation to each of the boxplots using the example provided in the boxplot function? boxplot(len ~ dose, data = ToothGrowth, boxwex = 0.25, at = 1:3 - 0.2, subset = supp == "VC", col = "yellow", main = "Guinea Pigs' Tooth Growth", xlab = "Vitamin C dose mg",

Dear R users I?m aware that the package ?modest? is useful to find the mode in an array. However, I?d like to know if someone has translated the ?mode? function built-in in MATLAB into R language. This function finds the most frequent value in an array (http://www.mathworks.com/help/techdoc/ref/mode.html). Best Fer

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") >

Hi, I have been using the lqs function with method='lms'. However the results I get are a little different from the results noted by Rousseeuw & Leroy (Robust Regression and Outlier Detection) and I was wondering how to use these results for outlier detection. I'm using the stackloss dataset, for which the original Rousseeuw et al. program points out that observations 1,2,3,4

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

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 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 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'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 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 =

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 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

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

Hola amigos, esta es mi primera duda, espero que no sea demasiado fácil. Tengo unos datos de dos variables y quiero mostrar recta de regresión y valor de correlación serie0 <- c(0.651, 0.712, 0.614, 0.645, 0.559, 0.647, 0.642, 0.534, 0.616, 0.621, 0.623) serie1 <- c(0.572, 0.641, 0.565, 0.596, 0.518, 0.604, 0.602, 0.501, 0.58, 0.589, 0.596) data <- cbind(serie0, serie1) colnames(data)

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

Hi, I want to remove the outliers of my database with the following program (an observation is considered an outlier if it is bigger than second quartile + 1,5* distance interquartiles or less than second quartile - 1,5*distance interquartiles): for(i in 1:length(dados)){ q3=quantile(dados[i], probs=.75) q3=quantile(dados[i], probs=.50) q1=quantile(dados[i], probs=.25) d=q3-q1 for(i2 in

Dear R-helpers, Very small amount of outliers can greatly affect the mean and many other statistic of a numeric variable. So, usually we must deal with the outliers properly in the process of data analysis. Here, I want to replace outliers with the group median of the variable. But, I can not construct a good way to do that efficiently, because of I am a newbie to R and programming. Can anybody

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