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Hello, I am running a Proc PLS to find the correlation between my dependent and independent variables, e.g Yield vs MinT MaxT. I am confused as to which of the tables tells me the percentage of my variables (min T and Max T combined and separately) that explained yield. My model was fine and below are some of my results.
Please how do I get the percentage of yield explained by a combination of MinT and MaxT
The PLS Procedure | ||||
SC=DRY | ||||
Percent Variation Accounted for by Partial Least Squares Factors | ||||
Number of Extracted Factors | Model Effects | Dependent Variables | ||
Current | Total | Current | Total | |
1 | 40.0228 | 40.0228 | 4.749 | 4.749 |
2 | 59.9772 | 100 | 0.0097 | 4.7587 |
SC=DRY | |||
Model Effect Loadings | |||
Number of Extracted Factors | MinT | MaxT | |
1 | 0.583192 | -0.81234 | |
2 | 0.778905 | 0.627142 | |
Model Effect Weights | |||
Number of Extracted Factors | MinT | MaxT | Inner Regression Coefficients |
1 | 0.628099 | -0.78009 | 0.243576 |
2 | 0.778905 | 0.627142 | 0.008972 |
Dependent Variable Weights | |||
Number of Extracted Factors | Wheat_Y | ||
1 | 1 | ||
2 | 1 |
Accepted Solutions
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PLS creates dimensions (sometimes called "factors" or "latent factors") which are used to predict Y. So, dimension 1 (which consists of a weighted combination of both minT and maxT) explains 4.749 percent of the Y variability, and dimension 2 (which consists of a weighted combination of both minT and maxT) explains another 0.0097% of the variability of Y. So I think the answer to your exact question "Please how do I get the percentage of yield explained by a combination of MinT and MaxT" is given by this number, but you have to decide if you want to use one dimension, or two dimensions.
You can obtain information that a certain percent of the variability of minT is used in dimension 1, and additional percent of variability of minT is used in dimension 2 (same is possible for maxT). You would add the VARSS option to the PROC PLS statement.
Paige Miller
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PLS creates dimensions (sometimes called "factors" or "latent factors") which are used to predict Y. So, dimension 1 (which consists of a weighted combination of both minT and maxT) explains 4.749 percent of the Y variability, and dimension 2 (which consists of a weighted combination of both minT and maxT) explains another 0.0097% of the variability of Y. So I think the answer to your exact question "Please how do I get the percentage of yield explained by a combination of MinT and MaxT" is given by this number, but you have to decide if you want to use one dimension, or two dimensions.
You can obtain information that a certain percent of the variability of minT is used in dimension 1, and additional percent of variability of minT is used in dimension 2 (same is possible for maxT). You would add the VARSS option to the PROC PLS statement.
Paige Miller
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