Principal Components
Eigenanalysis - Coefficients
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Principal ComponentsEigenanalysis - Coefficients |
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The principal components are the linear combinations of the original variables that account for the variance in the data. The maximum number of components extracted always equals the number of variables. The eigenvectors, which are comprised of coefficients corresponding to each variable, are used to calculate the principal component scores. The coefficients indicate the relative weight of each variable in the component. The bigger the absolute value of the coefficient, the more important the corresponding variable is in constructing the component.
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Note |
You must standardize the variables to obtain the correct component score. |
Example Output |
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Income 0.314 0.145 -0.676 -0.347 -0.241 Education 0.237 0.444 -0.401 0.240 0.622 Age 0.484 -0.135 -0.004 -0.212 -0.175 Residence 0.466 -0.277 0.091 0.116 -0.035 Employ 0.459 -0.304 0.122 -0.017 -0.014 Savings 0.404 0.219 0.366 0.436 0.143 Debt -0.067 -0.585 -0.078 -0.281 0.681 Credit cards -0.123 -0.452 -0.468 0.703 -0.195
Income 0.494 0.018 -0.030 Education -0.357 0.103 0.057 Age -0.487 -0.657 -0.052 Residence -0.085 0.487 -0.662 Employ -0.023 0.368 0.739 Savings 0.568 -0.348 -0.017 Debt 0.245 -0.196 -0.075 Credit cards -0.022 -0.158 0.058 |
Interpretation |
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For the loan applicant data, the first principal component's scores are computed from the original data using the coefficients listed under PC1:
PC1 = 0.314 Income + 0.237 Education + 0.484 Age + 0.466 Residence + 0.459 Employ + 0.404 Savings - 0.067 Debt - 0.123 Credit cards
The interpretation of the principal components is subjective and requires knowledge of the data:
