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 |
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.
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.
Are you ready for the spotlight? We're accepting content ideas for SAS Innovate 2025 to be held May 6-9 in Orlando, FL. The call is open until September 16. Read more here about why you should contribute and what is in it for you!
Learn the difference between classical and Bayesian statistical approaches and see a few PROC examples to perform Bayesian analysis in this video.
Find more tutorials on the SAS Users YouTube channel.
Select SAS Training centers are offering in-person courses. View upcoming courses for: