Identify outliers in a variableAkash Jain
outlierKD <- function(dt, var) { var_name <- eval(substitute(var),eval(dt)) na1 <- sum(is.na(var_name)) m1 <- mean(var_name, na.rm = T) par(mfrow=c(2, 2), oma=c(0,0,3,0)) boxplot(var_name, main="With outliers") hist(var_name, main="With outliers", xlab=NA,...
In the script below, I will plot the data with and without the outliers. Finally, with help from Selva, I added a question to ask whether to keep or remove the outliers in data. If the answer is yes then outliers will be replaced with NA. Here it is the function and an example ...
This work aimed at proposing a procedure based on the cumulative distribution of maximums and minimums to identify outliers in generalized Gamma-response models. In order to validate such method, we used simulations scenarios defined by the combination of different samples, contamination rate and ...
Identify and replace outliers and missing values in a time seriesRob J Hyndman
A general t-norm function maps the unit square in a Cartesian plane to the closed unit interval [0,1][0,1] in ℝR, i.e., 𝑑:[0,1]×[0,1]→[0,1]d:[0,1]×[0,1]→[0,1]. T-norm functions are used recursively to combine the three anomaly scores into a final one. ...