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[[x11, x12,...], [x21, x22, …], …],
α) returns those elements of [x1, x2,
…] larger than the α quantile
and smaller than the 1 - α quantile
of this list.
α, i) and
x12,...], [x21, x22,...],...]),
α, i) works on the i-th
entries of the input rows.
Measurement data often contains “outliers,” sample points rather far outside the range containing the majority of the points. While expected both from theory and experience, these outliers, for small or medium-sized samples, tend to distort statistical data, such as the mean value.
One of the standard methods dealing with this problem for (real)
continuous scales is discarding the outliers.
all data points below or above a given quantile.
Create a normally distributed sample, slightly contaminated:
r := stats::normalRandom(0, 1, Seed=2): data := [r() $ i = 1..300, 100*r() $ i = 1..2]:
The two extra points distort the data significantly:
stats::cutoff removes this noise and
the image shows more detail:
plot(plot::Histogram2d(stats::cutoff(data, 1/100), Cells=20))
stats::cutoff reduces the standard deviation
of the sample. Keeping in mind that the standard deviation of the
random number generator is 1,
compute that of the data in its various forms:
stats::stdev(data), stats::stdev(stats::cutoff(data, 1/20))
Statistical data: arithmetical expressions. The data to filter on must be real-valued.
Sample of type
Cutoff parameter: a real-valued expression .
Column index: positive integer. The nested list or the sample is filtered on its i-th column.
The input data with outliers being removed.