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 interquartile range (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

Data Outliers

Read Outliers

Show Outliers

Typeable Outliers

Generic Outliers

NFData Outliers

Monoid Outliers

Binary Outliers

data OutlierEffect

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 OutlierEffect

Data OutlierEffect

Ord OutlierEffect

Read OutlierEffect

Show OutlierEffect

Typeable OutlierEffect

Generic OutlierEffect

NFData OutlierEffect

Binary OutlierEffect

data OutlierVariance

Analysis of the extent to which outliers in a sample affect its standard deviation (and to some extent, its mean).

Constructors

OutlierVariance

Fields

ovEffect :: OutlierEffect

Qualitative description of effect.

ovDesc :: String

Brief textual description of effect.

ovFraction :: Double

Quantitative description of effect (a fraction between 0 and 1).

Instances

Eq OutlierVariance

Data OutlierVariance

Read OutlierVariance

Show OutlierVariance

Typeable OutlierVariance

Generic OutlierVariance

NFData OutlierVariance

Binary OutlierVariance

data SampleAnalysis

Result of a bootstrap analysis of a non-parametric sample.

Constructors

SampleAnalysis

Fields

anMean :: Estimate

anStdDev :: Estimate

anOutlierVar :: OutlierVariance

Instances

Eq SampleAnalysis

Data SampleAnalysis

Read SampleAnalysis

Show SampleAnalysis

Typeable SampleAnalysis

Generic SampleAnalysis

NFData SampleAnalysis

Binary SampleAnalysis

analyseSample

Arguments

:: Double

Confidence interval (between 0 and 1).

-> Sample

Sample data.

-> Int

Number of resamples to perform when bootstrapping.

-> IO SampleAnalysis

Perform a bootstrap analysis of a non-parametric sample.

scale

Arguments

:: Double

Value to multiply by.

-> SampleAnalysis

-> SampleAnalysis

Multiply the Estimates in an analysis by the given value, using scale.

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

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