similar to: How to replace outliers by group median?

Dear R-helpers, I want to make a series of boxplots on several numeric univariates with two group variables (species and population, population nested in species, and with population as the X-axis). In order to get a proper order of the individual populations in X-axis, I need to assign a wanted order to the factor (population). I used the levels() function to do this assignment, but it seemed

Hi R-listers, I am new to R and programming. I have a large dataframe composed of two grouping variables (species, population, with populations nested in species) and tens of continuously numeric variables. For each numeric variable, I want to make a boxplot with population as the X axis and the boxes filled according to which species it is belonging to. But, that is a definitely tedious work. I

Dear Ruser's I ask for helps on how to substitute missing values (NAs) by mean of the group it is belonging to. my dummy dataframe is: > df group traits 1 BSPy01-10 NA 2 BSPy01-10 7.3 3 BSPy01-10 7.3 4 BSPy01-11 5.3 5 BSPy01-11 5.4 6 BSPy01-11 5.6 7 BSPy01-11 NA 8 BSPy01-11 NA 9 BSPy01-11 4.8 10 BSPy01-12 8.1 11 BSPy01-12 6.0 12

Dear Ruser's I want to substitute each "NA" by the group mean of which the "NA" is belonging to. For example, substitute the first record of traits "NA" by the mean of "BSPy01-10" in the dummy dataframe. I have ever tried to solve this problem by using doBy package. But, I failed. I ask for the advice on how to use "lapplyBy" function of

Hi I am looking for suitable packages in R that do regression analyses using least median squares method (or better). Additionally, I am also looking for packages that implement algorithms/methods for detecting outliers that can be discarded before doing the regression analyses. Although some websites refer to "lms" method under package "lps" in R, I am unable to find such a

Hi, I am a new user of R. This is a conceptual doubt regarding screeing out outliers from the dataset in regression. I read up that Cook's distance can be used and if we want to remove influential observations, we can use the metric (>4/n) (n=no of observations) to remove any outliers. I also came across Grubb's test to identify outliers in univariate distns. (assumed normal) but i

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

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 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 <-

Hi Experts, I am new to R, using following sample code for capping outliers using percentile information. Working on large data (30000 observations and 150 variables), loop I am using in the below mentioned code for detecting outliers and capping to upper /lower percentile value is taking much time for the execution. Is there anything wrong with code, can anyone suggest improvement in the script

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.

Hallo there, greetings from Germany. I have a simple question for you. I have run a binary logistic model, but there are lots of outliers distorting the real results. I have tried to get rid of the outliers using the following commands: remove = -c(56, 303, 365, 391, 512, 746, 859, 940, 1037, 1042, 1138, 1355) MIGRATION.rebuild <- glm(MIGRATION, subset=remove)

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

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.

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,

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

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

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)

Hi friends, How to detect outliers and high leverage points for GLM ? Could I use plot(model) (i) "Residuals vs Fitted" graph to detect the outliers ? (ii) "Residuals vs Leverage" graph to detect the high leverage points ? And then remove those points from the data and re-run the model ? [[alternative HTML version deleted]]