Criterion.Analysis

Analysis code for benchmarks.

Documentation

data Outliers

Outliers from sample data, calculated using the boxplot technique.

Constructors

Outliers

Fields

samplesSeen :: !Int64

lowSevere :: !Int64

More than 3 times the IQR below the first quartile.

lowMild :: !Int64

Between 1.5 and 3 times the IQR below the first quartile.

highMild :: !Int64

Between 1.5 and 3 times the IQR above the third quartile.

highSevere :: !Int64

More than 3 times the IQR above the third quartile.

Instances

Eq Outliers

Read Outliers

Show Outliers

Monoid Outliers

data OutlierVariance

A description of the extent to which outliers in the sample data affect the sample mean and standard deviation.

Constructors

Unaffected

Less than 1% effect.

Slight

Between 1% and 10%.

Moderate

Between 10% and 50%.

Severe

Above 50% (i.e. measurements are useless).

Instances

Eq OutlierVariance

Ord OutlierVariance

Show OutlierVariance

analyseMean

Arguments

:: Sample

-> Int

Number of iterations used to compute the sample.

-> Criterion Double

Display the mean of a Sample, and characterise the outliers present in the sample.

countOutliers :: Outliers -> Int64

Count the total number of outliers in a sample.

classifyOutliers :: Sample -> Outliers

Classify outliers in a data set, using the boxplot technique.

noteOutliers :: Outliers -> Criterion ()

Display a report of the Outliers present in a Sample.

outlierVariance

Arguments

:: Estimate

Bootstrap estimate of sample mean.

-> Estimate

Bootstrap estimate of sample standard deviation.

-> Double

Number of original iterations.

-> (OutlierVariance, Double)

Compute the extent to which outliers in the sample data affect the sample mean and standard deviation.