stats

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

type AlternativeHypothesis string

type BartlettTestResult struct {
	ChiSquare        float64 // Chi-square statistic
	DegreesOfFreedom int     // Degrees of freedom
	PValue           float64 // P-value
	SampleSize       int     // Sample size
}

type ChiSquareTestResult struct {

	// a DataTable representing the contingency table([2]float64{observed, expected})
	ContingencyTable *insyra.DataTable
	// contains filtered or unexported fields
}

type CorrelationMethod int

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

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

type ExponentialRegressionResult struct {
	Intercept              float64   // coefficient a in y = a·e^(b·x)
	Slope                  float64   // coefficient b in y = a·e^(b·x)
	Residuals              []float64 // yᵢ − ŷᵢ
	RSquared               float64   // coefficient of determination
	AdjustedRSquared       float64   // adjusted R²
	StandardErrorIntercept float64   // standard error of coefficient a
	StandardErrorSlope     float64   // standard error of coefficient b
	TValueIntercept        float64   // t statistic for coefficient a
	TValueSlope            float64   // t statistic for coefficient b
	PValueIntercept        float64   // p-value for coefficient a
	PValueSlope            float64   // p-value for coefficient b

	// Confidence intervals for coefficients (95% by default)
	ConfidenceIntervalIntercept [2]float64 // confidence interval for intercept [lower, upper]
	ConfidenceIntervalSlope     [2]float64 // confidence interval for slope [lower, upper]
}

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

type FactorAnalysisOptions struct {
	Count      FactorCountSpec
	Extraction FactorExtractionMethod
	Rotation   FactorRotationOptions
	Scoring    FactorScoreMethod
	MaxIter    int     // Optional: default 100
	MinErr     float64 // Optional: default 0.001 (R's min.err)
}

type FactorAnalysisResult struct {
	Loadings             insyra.IDataTable   // Loading matrix (variables × factors)
	UnrotatedLoadings    insyra.IDataTable   // Unrotated loading matrix (variables × factors)
	Structure            insyra.IDataTable   // Structure matrix (variables × factors)
	Uniquenesses         insyra.IDataTable   // Uniqueness vector (p × 1)
	Communalities        insyra.IDataTable   // Communality table (p × 1: Extraction)
	SamplingAdequacy     insyra.IDataTable   // KMO overall index and per-variable MSA values
	BartlettTest         *BartlettTestResult // Bartlett's test of sphericity summary
	Phi                  insyra.IDataTable   // Factor correlation matrix (m × m), nil for orthogonal
	RotationMatrix       insyra.IDataTable   // Rotation matrix (m × m), nil if no rotation
	Eigenvalues          insyra.IDataTable   // Eigenvalues vector (p × 1)
	ExplainedProportion  insyra.IDataTable   // Proportion explained by each factor (m × 1)
	CumulativeProportion insyra.IDataTable   // Cumulative proportion explained (m × 1)
	Scores               insyra.IDataTable   // Factor scores (n × m), nil if not computed
	ScoreCoefficients    insyra.IDataTable   // Factor score coefficient matrix (variables × factors)
	ScoreCovariance      insyra.IDataTable   // Factor score covariance matrix (factors × factors)

	Converged         bool
	RotationConverged bool
	Iterations        int
	CountUsed         int
	Messages          []string
}

type FactorCountMethod string

type FactorCountSpec struct {
	Method         FactorCountMethod
	FixedK         int     // Optional: used when Method is CountFixed
	EigenThreshold float64 // Optional: default 1.0 for CountKaiser
	MaxFactors     int     // Optional: 0 means no limit
}

type FactorExtractionMethod string

type FactorModel struct {
	FactorAnalysisResult
	// contains filtered or unexported fields
}

type FactorRotationMethod string

type FactorRotationOptions struct {
	Method           FactorRotationMethod
	Kappa            float64          // Optional: Promax power (default 4)
	Delta            float64          // Optional: default 0 for Oblimin
	Restarts         int              // Optional: random orthonormal starts for GPA rotations (default 10)
	VarimaxAlgorithm VarimaxAlgorithm // Optional: "gparotation" (default) or "kaiser" (SPSS-compatible)
}

type FactorScoreMethod string

type KurtosisMethod int

type LinearRegressionResult struct {
	Slope                  float64 // regression coefficient β₁ (for simple regression)
	Intercept              float64 // regression coefficient β₀ (for simple regression)
	StandardError          float64 // SE(β₁) - slope standard error (for simple regression)
	StandardErrorIntercept float64 // SE(β₀) - intercept standard error (for simple regression)
	TValue                 float64 // t statistic for β₁ (for simple regression)
	TValueIntercept        float64 // t statistic for β₀ (for simple regression)
	PValue                 float64 // two-tailed p-value for β₁ (for simple regression)
	PValueIntercept        float64 // two-tailed p-value for β₀ (for simple regression)

	// Legacy confidence intervals for simple regression compatibility
	ConfidenceIntervalIntercept [2]float64 // 95% confidence interval for intercept [lower, upper]
	ConfidenceIntervalSlope     [2]float64 // 95% confidence interval for slope [lower, upper]

	// Extended fields for multiple regression
	Coefficients   []float64 // [β₀, β₁, ..., βₚ] (intercept + slopes)
	StandardErrors []float64 // standard errors for each coefficient
	TValues        []float64 // t statistics for each coefficient
	PValues        []float64 // two-tailed p-values for each coefficient
	// Confidence intervals for coefficients (95% by default)
	ConfidenceIntervals [][2]float64 // confidence intervals for each coefficient [lower, upper]

	// Common fields
	Residuals        []float64 // yᵢ − ŷᵢ
	RSquared         float64   // coefficient of determination
	AdjustedRSquared float64   // adjusted R²
}

type LogarithmicRegressionResult struct {
	Intercept              float64   // intercept coefficient in y = a + b·ln(x)
	Slope                  float64   // slope coefficient in y = a + b·ln(x)
	Residuals              []float64 // yᵢ − ŷᵢ
	RSquared               float64   // coefficient of determination
	AdjustedRSquared       float64   // adjusted R²
	StandardErrorIntercept float64   // standard error of coefficient a
	StandardErrorSlope     float64   // standard error of coefficient b
	TValueIntercept        float64   // t statistic for coefficient a
	TValueSlope            float64   // t statistic for coefficient b
	PValueIntercept        float64   // p-value for coefficient a
	PValueSlope            float64   // p-value for coefficient b
	// Confidence intervals for coefficients (95% by default)
	ConfidenceIntervalIntercept [2]float64 // confidence interval for intercept [lower, upper]
	ConfidenceIntervalSlope     [2]float64 // confidence interval for slope [lower, upper]
}

type OneWayANOVAResult struct {
	Factor  ANOVAResultComponent
	Within  ANOVAResultComponent
	TotalSS float64
}

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
}

type PolynomialRegressionResult struct {
	Coefficients     []float64 // polynomial coefficients [a₀, a₁, a₂, ...]
	Degree           int       // degree of polynomial
	Residuals        []float64 // yᵢ − ŷᵢ
	RSquared         float64   // coefficient of determination
	AdjustedRSquared float64   // adjusted R²
	StandardErrors   []float64 // standard errors for each coefficient
	TValues          []float64 // t statistics for each coefficient
	PValues          []float64 // p-values for each coefficient
	// Confidence intervals for coefficients (95% by default)
	ConfidenceIntervals [][2]float64 // confidence intervals for each coefficient [lower, upper]
}

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

type SkewnessMethod int

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
}

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

type VarimaxAlgorithm string

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
}