In statistics, or particularly in R programming, an outlier is defined as an observation that is far removed from most of the other observations. Often, an outlier is present due to a measurement error.
The following script is used to detect the particular outliers for each and every attribute:
> 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, ylab=NA) + outlier <- boxplot.stats(var_name)$out + mo <- mean(outlier) + var_name <- ifelse(var_name %in% outlier, NA, var_name) + boxplot(var_name, ...
