| Copyright | (c) 2009-2014 Bryan O'Sullivan |
|---|---|
| License | BSD-style |
| Maintainer | bos@serpentine.com |
| Stability | experimental |
| Portability | GHC |
| Safe Haskell | Trustworthy |
| Language | Haskell2010 |
Criterion.Analysis
Description
Analysis code for benchmarks.
Synopsis
- data Outliers = Outliers {
- samplesSeen :: !Int64
- lowSevere :: !Int64
- lowMild :: !Int64
- highMild :: !Int64
- highSevere :: !Int64
- data OutlierEffect
- = Unaffected
- | Slight
- | Moderate
- | Severe
- data OutlierVariance = OutlierVariance {}
- data SampleAnalysis = SampleAnalysis {}
- analyseSample :: Int -> String -> Vector Measured -> ExceptT String Criterion Report
- scale :: Double -> SampleAnalysis -> SampleAnalysis
- analyseMean :: Sample -> Int -> Criterion Double
- countOutliers :: Outliers -> Int64
- classifyOutliers :: Sample -> Outliers
- noteOutliers :: Outliers -> Criterion ()
- outlierVariance :: Estimate ConfInt Double -> Estimate ConfInt Double -> Double -> OutlierVariance
- resolveAccessors :: [String] -> Either String [(String, Measured -> Maybe Double)]
- validateAccessors :: [String] -> String -> Either String [(String, Measured -> Maybe Double)]
- regress :: GenIO -> [String] -> String -> Vector Measured -> ExceptT String Criterion Regression
Documentation
Outliers from sample data, calculated using the boxplot technique.
Constructors
| Outliers | |
Fields
| |
Instances
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
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
| |
Instances
data SampleAnalysis #
Result of a bootstrap analysis of a non-parametric sample.
Constructors
| SampleAnalysis | |
Fields
| |
Instances
Arguments
| :: Int | Experiment number. |
| -> String | Experiment name. |
| -> Vector Measured | Sample data. |
| -> ExceptT String Criterion Report |
Perform an analysis of a measurement.
Arguments
| :: Double | Value to multiply by. |
| -> SampleAnalysis | |
| -> SampleAnalysis |
Multiply the Estimates in an analysis by the given value, using
scale.
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.
Arguments
| :: Estimate ConfInt Double | Bootstrap estimate of sample mean. |
| -> Estimate ConfInt Double | 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.
resolveAccessors :: [String] -> Either String [(String, Measured -> Maybe Double)] #
Given a list of accessor names (see measureKeys), return either
a mapping from accessor name to function or an error message if
any names are wrong.
Arguments
| :: [String] | Predictor names. |
| -> String | Responder name. |
| -> Either String [(String, Measured -> Maybe Double)] |
Given predictor and responder names, do some basic validation, then hand back the relevant accessors.
Arguments
| :: GenIO | |
| -> [String] | Predictor names. |
| -> String | Responder name. |
| -> Vector Measured | |
| -> ExceptT String Criterion Regression |
Regress the given predictors against the responder.
Errors may be returned under various circumstances, such as invalid names or lack of needed data.
See olsRegress for details of the regression performed.