Typedef
Static Public Summary | ||
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KComplexInputData: Complex | number | boolean | string | Array<number> | {_re: number, _im: number} | {doubleValue: number} | {toString: function} Complex type argument. |
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Matrix type argument. |
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Collection of calculation settings for matrix. |
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Analysis of variance. |
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Output for multiple regression analysis |
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Regression table. |
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Regression table data. |
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Settings for multiple regression analysis |
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Vector state |
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Output for principal component analysis. |
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Settings for principal component analysis. |
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Collection of calculation settings for matrix. |
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Collection of calculation settings for matrix. |
Static Public
public KComplexInputData: Complex | number | boolean | string | Array<number> | {_re: number, _im: number} | {doubleValue: number} | {toString: function} source
Complex type argument.
- Complex
- number
- boolean
- string
- Array<number>
- {_re:number,_im:number}
- {doubleValue:number}
- {toString:function}
Initialization can be performed as follows.
- 1200, "1200", "12e2", "1.2e3"
- "3 + 4i", "4j + 3", [3, 4].
public KMatrixInputData: Complex source
Matrix type argument.
- Matrix
- Complex
- number
- string
- Array<string|number|Complex|Matrix>
- Array<Array<string|number|Complex|Matrix>>
- {doubleValue:number}
- {toString:function}
Initialization can be performed as follows.
- 10, "10", "3 + 4j", "[ 1 ]", "[1, 2, 3]", "[1 2 3]", [1, 2, 3],
- [[1, 2], [3, 4]], "[1 2; 3 4]", "[1+2i 3+4i]",
- "[1:10]", "[1:2:3]" (MATLAB / Octave / Scilab compatible).
public KMatrixSettings: Object source
Collection of calculation settings for matrix.
- Available options vary depending on the method.
public KMultipleRegressionAnalysisAnova: Object source
Analysis of variance. ANOVA.
Properties:
Name | Type | Attribute | Description |
regression | KMultipleRegressionAnalysisVectorState | regression. |
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residual | KMultipleRegressionAnalysisVectorState | residual error. |
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total | KMultipleRegressionAnalysisVectorState | total. |
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F | number | F value. Dispersion ratio (F0) |
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significance_F | number | Significance F. Test with F distribution with q, n-q-1 degrees of freedom.(Probability of error.) |
public KMultipleRegressionAnalysisOutput: Object source
Output for multiple regression analysis
Properties:
Name | Type | Attribute | Description |
q | number | number of explanatory variables. |
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n | number | number of samples. |
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predicted_values | number[][] | predicted values. (column vector) |
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sY | number | Variance of predicted values of target variable. |
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sy | number | Variance of measured values of target variable. |
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multiple_R | number | Multiple R. Multiple correlation coefficient. |
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R_square | number | R Square. Coefficient of determination. |
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adjusted_R_square | number | Adjusted R Square. Adjusted coefficient of determination. |
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ANOVA | KMultipleRegressionAnalysisAnova | analysis of variance. |
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Ve | number | Unbiased variance of residuals. (Ve) |
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standard_error | number | Standard error. (SE) |
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AIC | number | Akaike's Information Criterion. (AIC) |
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regression_table | KMultipleRegressionAnalysisPartialRegression | Regression table. |
public KMultipleRegressionAnalysisPartialRegression: Object source
Regression table.
Properties:
Name | Type | Attribute | Description |
intercept | KMultipleRegressionAnalysisPartialRegressionData | Intercept. |
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parameters | KMultipleRegressionAnalysisPartialRegressionData[] | Parameters. |
public KMultipleRegressionAnalysisSettings: Object source
Settings for multiple regression analysis
Properties:
Name | Type | Attribute | Description |
samples | KMatrixInputData | explanatory variable. (Each column is a parameters and each row is a samples.) |
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target | KMatrixInputData | response variable. / actual values. (column vector) |
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is_standardised | boolean |
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Use standardized partial regression coefficients. |
public KPrincipalComponent: Object source
Properties:
Name | Type | Attribute | Description |
eigen_value | number | Contribution. Eigen value. Variance of principal components. |
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factor_loading | number[] | Factor loading. Eigen vector. Principal component coefficients. |
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factor_loading_contribution_rate | number[] | Factor loading contribution rate. |
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cumulative_contribution_ratio | number | Cumulative contribution ratio. |
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contribution_ratio | number | Contribution ratio. |
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score | number[] | Principal component score. |
public KPrincipalComponentAnalysisOutput: Object source
Output for principal component analysis.
Properties:
Name | Type | Attribute | Description |
principal_component | KPrincipalComponent[] | Principal component. |
public KPrincipalComponentAnalysisSettings: Object source
Settings for principal component analysis.
Properties:
Name | Type | Attribute | Description |
samples | KMatrixInputData | explanatory variable. (Each column is a parameters and each row is a samples.) |
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is_unbiased | boolean |
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Use unbiased variance when calculating variance from samples. |
is_standardised | boolean |
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Use standardized explanatory variables. Use the correlation matrix instead of the covariance matrix. |
public KSignalSettings: Object source
Collection of calculation settings for matrix.
- Available options vary depending on the method.