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Nick Cox
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R: How to handle strongly correlated but not perfectly collinear dummys?dummies

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Pugl
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I am using several dummy variables in a GLM model implemented in R with a logit link function.

However, the coefficient of one of the dummy variables is not shown in the results with the warning that "Coefficients: (1 not defined because of singularities)".

When I check the correlations between the dummy variables with the cor() function, I see that 2 of them are indeed strongly, though not perfectly correlated (-0.6..).

I understand that one of the dummy variables could be removed if we had perfect collinearity. But how can this be handled in a situation where the correlation is "only" strong but not perfect without losing information (as I can not perfectly predict the correlated dummy)?

I am using several dummy variables in a GLM model implemented in R with a logit link function.

However, one of the dummy variables is not shown in the results with the warning that "Coefficients: (1 not defined because of singularities)".

When I check the correlations between the dummy variables with the cor() function, I see that 2 of them are indeed strongly, though not perfectly correlated (-0.6..).

I understand that one of the dummy variables could be removed if we had perfect collinearity. But how can this be handled in a situation where the correlation is "only" strong but not perfect without losing information?

I am using several dummy variables in a GLM model implemented in R with a logit link function.

However, the coefficient of one of the dummy variables is not shown in the results with the warning that "Coefficients: (1 not defined because of singularities)".

When I check the correlations between the dummy variables with the cor() function, I see that 2 of them are indeed strongly, though not perfectly correlated (-0.6..).

I understand that one of the dummy variables could be removed if we had perfect collinearity. But how can this be handled in a situation where the correlation is "only" strong but not perfect without losing information (as I can not perfectly predict the correlated dummy)?

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Pugl
  • 1.5k
  • 3
  • 24
  • 45

R: How to handle strongly correlated but not perfectly collinear dummys?

I am using several dummy variables in a GLM model implemented in R with a logit link function.

However, one of the dummy variables is not shown in the results with the warning that "Coefficients: (1 not defined because of singularities)".

When I check the correlations between the dummy variables with the cor() function, I see that 2 of them are indeed strongly, though not perfectly correlated (-0.6..).

I understand that one of the dummy variables could be removed if we had perfect collinearity. But how can this be handled in a situation where the correlation is "only" strong but not perfect without losing information?