Portability | GHC |
---|---|
Stability | experimental |
Maintainer | bos@serpentine.com |
Analysis code for benchmarks.
- data Outliers = Outliers {
- samplesSeen :: !Int64
- lowSevere :: !Int64
- lowMild :: !Int64
- highMild :: !Int64
- highSevere :: !Int64
- data OutlierVariance
- = Unaffected
- | Slight
- | Moderate
- | Severe
- analyseMean :: Sample -> Int -> Criterion Double
- countOutliers :: Outliers -> Int64
- classifyOutliers :: Sample -> Outliers
- noteOutliers :: Outliers -> Criterion ()
- outlierVariance :: Estimate -> Estimate -> Double -> (OutlierVariance, Double)
Documentation
Outliers from sample data, calculated using the boxplot technique.
Outliers | |
|
data OutlierVariance Source
A description of the extent to which outliers in the sample data affect the sample mean and standard deviation.
Unaffected | Less than 1% effect. |
Slight | Between 1% and 10%. |
Moderate | Between 10% and 50%. |
Severe | Above 50% (i.e. measurements are useless). |
Display the mean of a Sample
, and characterise the outliers
present in the sample.
countOutliers :: Outliers -> Int64Source
Count the total number of outliers in a sample.
classifyOutliers :: Sample -> OutliersSource
Classify outliers in a data set, using the boxplot technique.
:: 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.