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By using the PLS Regression, you can follow thisthese steps:

  1. Apply the algorithm by using a validation method (iI recommend the full cross validation);
  2. Select the number of latent variables that the validation gives to you;
  3. Re-run the plsPLS algorithm with the number of latent variables in step 2 and re-apply the cross validation.

With thisthese 3 steps, you can obtain an $\hat{y}$ but the $X$ now is the result of the combination of the old variables, the Score Matrix. Basically you can only compare the original $y$ with the new $\hat{y}$, because of the nature of the latent variables that you select.

By using the PLS Regression you can follow this steps:

  1. Apply the algorithm by using a validation method (i recommend the full cross validation);
  2. Select the number of latent variables that the validation gives to you;
  3. Re-run the pls algorithm with the number of latent variables in step 2 and re-apply the cross validation.

With this 3 steps you can obtain an $\hat{y}$ but the $X$ now is the result of the combination of the old variables, the Score Matrix. Basically you can only compare the original $y$ with the new $\hat{y}$, because of the nature of the latent variables that you select.

By using the PLS Regression, you can follow these steps:

  1. Apply the algorithm by using a validation method (I recommend the full cross validation);
  2. Select the number of latent variables that the validation gives to you;
  3. Re-run the PLS algorithm with the number of latent variables in step 2 and re-apply the cross validation.

With these 3 steps, you can obtain an $\hat{y}$ but the $X$ now is the result of the combination of the old variables, the Score Matrix. Basically you can only compare the original $y$ with the new $\hat{y}$, because of the nature of the latent variables that you select.

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By using the PLS Regression you can follow this steps:

  1. Apply the algorithm by using a validation method (i recommend the full cross validation);
  2. Select the number of latent variables that the validation gives to you;
  3. Re-run the pls algorithm with the number of latent variables in step 2 and re-apply the cross validation.

With this 3 steps you can obtain an $\hat{y}$ but the $X$ now is the result of the combination of the old variables, the Score Matrix. Basically you can only compare the original $y$ with the new $\hat{y}$, because of the nature of the latent variables that you select.