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This question is for CrossValidated but here are a few suggestions:

  1. Check multi-collinearity using VIF in the cars package between variables. VIF > 10 is not desirable.

If Multi Collinearity exists there are three options:

(1) Check the relationship of the variables with the response to understand what kind of transformation can be applied e.g. quadratic, polynomial, logarithmic etc.

(2) Use PCA to combine some of the variables. Here is how you can reproduce that in R : http://stackoverflow.com/questions/18139292/reproducing-spss-factor-analysis-with-r/25070213#25070213https://stackoverflow.com/questions/18139292/reproducing-spss-factor-analysis-with-r/25070213#25070213

(3) Remove the multi-collinear variables. Essentially the probability of a multi collinear impacting the outcome is low. you can observe this in the t-value of the variable. There are "*" symbols in the Pr(>|t|) field of the model output. It denotes the significance or the likelihood of the variable to not be influencing the response.

This question is for CrossValidated but here are a few suggestions:

  1. Check multi-collinearity using VIF in the cars package between variables. VIF > 10 is not desirable.

If Multi Collinearity exists there are three options:

(1) Check the relationship of the variables with the response to understand what kind of transformation can be applied e.g. quadratic, polynomial, logarithmic etc.

(2) Use PCA to combine some of the variables. Here is how you can reproduce that in R : http://stackoverflow.com/questions/18139292/reproducing-spss-factor-analysis-with-r/25070213#25070213

(3) Remove the multi-collinear variables. Essentially the probability of a multi collinear impacting the outcome is low. you can observe this in the t-value of the variable. There are "*" symbols in the Pr(>|t|) field of the model output. It denotes the significance or the likelihood of the variable to not be influencing the response.

This question is for CrossValidated but here are a few suggestions:

  1. Check multi-collinearity using VIF in the cars package between variables. VIF > 10 is not desirable.

If Multi Collinearity exists there are three options:

(1) Check the relationship of the variables with the response to understand what kind of transformation can be applied e.g. quadratic, polynomial, logarithmic etc.

(2) Use PCA to combine some of the variables. Here is how you can reproduce that in R : https://stackoverflow.com/questions/18139292/reproducing-spss-factor-analysis-with-r/25070213#25070213

(3) Remove the multi-collinear variables. Essentially the probability of a multi collinear impacting the outcome is low. you can observe this in the t-value of the variable. There are "*" symbols in the Pr(>|t|) field of the model output. It denotes the significance or the likelihood of the variable to not be influencing the response.

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vagabond
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This question is for CrossValidated but here are a few suggestions:

  1. Check multi-collinearity using VIF in the cars package between variables. VIF > 10 is not desirable.

If Multi Collinearity exists there are three options:

(1) Check the relationship of the variables with the response to understand what kind of transformation can be applied e.g. quadratic, polynomial, logarithmic etc.

(2) Use PCA to combine some of the variables. Here is how you can reproduce that in R : http://stackoverflow.com/questions/18139292/reproducing-spss-factor-analysis-with-r/25070213#25070213

(3) Remove the multi-collinear variables. Essentially the probability of a multi collinear impacting the outcome is low. you can observe this in the t-value of the variable. There are "*" symbols in the Pr(>|t|) field of the model output. It denotes the significance or the likelihood of the variable to not be influencing the response.