I have a data frame like this:
x
Team 01/01/2012 01/02/2012 01/03/2012 01/01/2012 01/04/2012 SD Mean
A 100 50 40 NA
Get your IQR (Interquartile range) and lower/upper quartile using:
lowerq = quantile(data)[2]
upperq = quantile(data)[4]
iqr = upperq - lowerq #Or use IQR(data)
Compute the bounds for a mild outlier:
mild.threshold.upper = (iqr * 1.5) + upperq
mild.threshold.lower = lowerq - (iqr * 1.5)
Any data point outside (> mild.threshold.upper or < mild.threshold.lower) these values is a mild outlier
To detect extreme outliers do the same, but multiply by 3 instead:
extreme.threshold.upper = (iqr * 3) + upperq
extreme.threshold.lower = lowerq - (iqr * 3)
Any data point outside (> extreme.threshold.upper or < extreme.threshold.lower) these values is an extreme outlier
Hope this helps
edit: was accessing 50%, not 75%