stats

package
v0.2.0 Latest Latest
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 Go to latest Published: May 29, 2025 License: MIT Imports: 11 Imported by: 1

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Index

Constants

This section is empty.

Variables

This section is empty.

Functions

func CalculateMoment added in v0.0.3 

func CalculateMoment(dl insyra.IDataList, n int, central bool) float64

CalculateMoment calculates the n-th moment of the DataList. If central is true, it computes the central moment; otherwise, raw moment. Returns NaN if the DataList is empty or the moment cannot be calculated.

func Covariance added in v0.0.4 

func Covariance(dlX, dlY insyra.IDataList) float64

func Kurtosis 

func Kurtosis(data any, method ...KurtosisMethod) float64

Kurtosis calculates the kurtosis of the DataList. method: 1 = g2, 2 = adjusted Fisher kurtosis, 3 = bias-adjusted. Default is KurtosisG2. Returns NaN if the data is empty or undefined.

func Skewness added in v0.0.3 

func Skewness(sample any, method ...SkewnessMethod) float64

Skewness calculates the skewness of a sample using the specified method.

method default: SkewnessG1（type 1）。

Types

type ANOVAResultComponent added in v0.2.0 

type ANOVAResultComponent struct {
	SumOfSquares float64
	DF           int
	F            float64
	P            float64
	EtaSquared   float64
}

type AlternativeHypothesis added in v0.2.0 

type AlternativeHypothesis string
const (
	TwoSided AlternativeHypothesis = "two-sided"
	Greater  AlternativeHypothesis = "greater"
	Less     AlternativeHypothesis = "less"
)

type ChiSquareTestResult added in v0.0.6 

type ChiSquareTestResult struct {
	// contains filtered or unexported fields
}

func ChiSquareGoodnessOfFit added in v0.2.0 

func ChiSquareGoodnessOfFit(input insyra.IDataList, p []float64, rescaleP bool) *ChiSquareTestResult

ChiSquareGoodnessOfFit performs a one-dimensional chi-square goodness of fit test.

func ChiSquareIndependenceTest added in v0.2.0 

func ChiSquareIndependenceTest(rowData, colData insyra.IDataList) *ChiSquareTestResult

ChiSquareIndependenceTest performs a chi-square test of independence.

type CorrelationMethod added in v0.0.4 

type CorrelationMethod int

CorrelationMethod 定義了相關係數的計算方法 

const (
	PearsonCorrelation CorrelationMethod = iota
	KendallCorrelation
	SpearmanCorrelation
)

type CorrelationResult added in v0.2.0 

type CorrelationResult struct {
	// contains filtered or unexported fields
}

func Correlation added in v0.0.4 

func Correlation(dlX, dlY insyra.IDataList, method CorrelationMethod) *CorrelationResult

type EffectSizeEntry added in v0.2.0 

type EffectSizeEntry struct {
	Type  string  // "cohen_d", "hedges_g", "glass_delta", etc.
	Value float64 // Effect size value
}

type FTestResult added in v0.0.6 

type FTestResult struct {
	DF2 float64 // degree of freedom for the second group
	// contains filtered or unexported fields
}

func BartlettTest added in v0.2.0 

func BartlettTest(groups []insyra.IDataList) *FTestResult

BartlettTest performs Bartlett's test for equality of variances. Input: slice of *insyra.DataList, each representing a group.

func FTestForNestedModels added in v0.2.0 

func FTestForNestedModels(rssReduced, rssFull float64, dfReduced, dfFull int) *FTestResult

FTestForNestedModels compares two nested regression models. rssReduced: residual sum of squares of reduced model rssFull: residual sum of squares of full model dfReduced, dfFull: degrees of freedom of both models

func FTestForRegression added in v0.2.0 

func FTestForRegression(ssr, sse float64, df1, df2 int) *FTestResult

FTestForRegression performs an overall F-test for a regression model. ssr: regression sum of squares sse: error sum of squares df1: degrees of freedom for the model (number of predictors) df2: degrees of freedom for residuals (n - k - 1)

func FTestForVarianceEquality added in v0.0.6 

func FTestForVarianceEquality(data1, data2 insyra.IDataList) *FTestResult

FTestForVarianceEquality performs an F-test for variance equality

func LeveneTest added in v0.2.0 

func LeveneTest(groups []insyra.IDataList) *FTestResult

LeveneTest performs Levene's Test for equality of variances across multiple groups. Input: slice of *insyra.DataList, each representing a group. Output: *FTestResult

type KurtosisMethod added in v0.2.0 

type KurtosisMethod int

KurtosisMethod defines available kurtosis calculation methods. 

const (
	KurtosisG2           KurtosisMethod = iota + 1 // Type 1: g2 (default)
	KurtosisAdjusted                               // Type 2: adjusted Fisher kurtosis
	KurtosisBiasAdjusted                           // Type 3: bias-adjusted
)

type LinearRegressionResult added in v0.0.4 

type LinearRegressionResult struct {
	Slope            float64   // 斜率
	Intercept        float64   // 截距
	Residuals        []float64 // 殘差
	RSquared         float64   // R-squared
	AdjustedRSquared float64   // 調整後的 R-squared
	StandardError    float64   // 標準誤差
	TValue           float64   // t 值
	PValue           float64   // p 值
}

LinearRegressionResult holds the result of a linear regression, including slope, intercept, and other statistical details.

func LinearRegression added in v0.0.4 

func LinearRegression(dlX, dlY insyra.IDataList) *LinearRegressionResult

LinearRegression performs simple linear regression on two datasets (X and Y). It returns the slope, intercept, residuals, R-squared, and other statistical details.

type OneWayANOVAResult added in v0.0.7 

type OneWayANOVAResult struct {
	Factor  ANOVAResultComponent
	Within  ANOVAResultComponent
	TotalSS float64
}

func OneWayANOVA added in v0.0.6 

func OneWayANOVA(groups ...insyra.IDataList) *OneWayANOVAResult

type PCAResult added in v0.0.8 

type PCAResult struct {
	Components        insyra.IDataTable // 主成分存為 DataTable
	Eigenvalues       []float64         // 對應的特徵值
	ExplainedVariance []float64         // 每個主成分解釋的變異百分比
}

PCAResult contains the results of a Principal Component Analysis.

func PCA added in v0.0.8 

func PCA(dataTable insyra.IDataTable, nComponents ...int) *PCAResult

PCA calculates the Principal Component Analysis of a DataTable. The function returns a PCAResult struct containing the principal components, eigenvalues, and explained variance. The number of components to extract can be specified using the nComponents parameter. If nComponents is not specified or exceeds the number of columns, all components will be extracted.

type RepeatedMeasuresANOVAResult added in v0.0.8 

type RepeatedMeasuresANOVAResult struct {
	Factor  ANOVAResultComponent
	Subject ANOVAResultComponent
	Within  ANOVAResultComponent
	TotalSS float64
}

func RepeatedMeasuresANOVA added in v0.2.0 

func RepeatedMeasuresANOVA(subjects ...insyra.IDataList) *RepeatedMeasuresANOVAResult

type SkewnessMethod added in v0.2.0 

type SkewnessMethod int

SkewnessMethod defines available skewness calculation methods. 

const (
	SkewnessG1           SkewnessMethod = iota + 1 // Type 1: G1 (default)
	SkewnessAdjusted                               // Type 2: Adjusted Fisher-Pearson
	SkewnessBiasAdjusted                           // Type 3: Bias-adjusted
)

type TTestResult added in v0.0.4 

type TTestResult struct {
	Mean     *float64 // mean of the first group (or the only group)
	Mean2    *float64 // mean of the second group (nil if not applicable)
	MeanDiff *float64 // mean difference (only for paired t-test)
	N        int      // sample size of the first group (or the only group or paired group)
	N2       *int     // sample size of the second group (nil if not applicable)
	// contains filtered or unexported fields
}

func PairedTTest added in v0.0.4 

func PairedTTest(data1, data2 insyra.IDataList, confidenceLevel float64) *TTestResult

func SingleSampleTTest added in v0.0.4 

func SingleSampleTTest(data insyra.IDataList, mu float64, confidenceLevel float64) *TTestResult

func TwoSampleTTest added in v0.0.4 

func TwoSampleTTest(data1, data2 insyra.IDataList, equalVariance bool, confidenceLevel float64) *TTestResult

type TwoWayANOVAResult added in v0.0.7 

type TwoWayANOVAResult struct {
	FactorA     ANOVAResultComponent
	FactorB     ANOVAResultComponent
	Interaction ANOVAResultComponent
	Within      ANOVAResultComponent
	TotalSS     float64
}

func TwoWayANOVA added in v0.2.0 

func TwoWayANOVA(factorALevels, factorBLevels int, cells ...insyra.IDataList) *TwoWayANOVAResult

type ZTestResult added in v0.2.0 

type ZTestResult struct {
	Mean  float64  // mean of the first group (or the only group)
	Mean2 *float64 // mean of the second group (nil if not applicable)
	N     int      // sample size of the first group (or the only group)
	N2    *int     // sample size of the second group (nil if not applicable)
	// contains filtered or unexported fields
}

func SingleSampleZTest added in v0.2.0 

func SingleSampleZTest(data insyra.IDataList, mu float64, sigma float64, alternative AlternativeHypothesis, confidenceLevel float64) *ZTestResult

func TwoSampleZTest added in v0.2.0 

func TwoSampleZTest(data1, data2 insyra.IDataList, sigma1, sigma2 float64, alternative AlternativeHypothesis, confidenceLevel float64) *ZTestResult

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