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To check the multicollinearity, the method I know is to use vif in car library. To use vif, we first have to fit a model. However, the data is $p > n$. We cannot fit a linear regression model with OLS. Then we try to fit in LASSO but all coefficients are 0. Hence, I cannot check the multicollinearity.

Is there a way to check multicollinearity without a model? For example, correlation matrix. But what if $x1 = x2 + x3$?

I have read this postpost . I am not clear how does PCA detect (rather than handling) multicollinearity.

To check the multicollinearity, the method I know is to use vif in car library. To use vif, we first have to fit a model. However, the data is $p > n$. We cannot fit a linear regression model with OLS. Then we try to fit in LASSO but all coefficients are 0. Hence, I cannot check the multicollinearity.

Is there a way to check multicollinearity without a model? For example, correlation matrix. But what if $x1 = x2 + x3$?

I have read this post . I am not clear how does PCA detect (rather than handling) multicollinearity.

To check the multicollinearity, the method I know is to use vif in car library. To use vif, we first have to fit a model. However, the data is $p > n$. We cannot fit a linear regression model with OLS. Then we try to fit in LASSO but all coefficients are 0. Hence, I cannot check the multicollinearity.

Is there a way to check multicollinearity without a model? For example, correlation matrix. But what if $x1 = x2 + x3$?

I have read this post . I am not clear how does PCA detect (rather than handling) multicollinearity.

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Jill Clover
Jill Clover
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To check the multicollinearity, the method I know is to use vif in car library. To use vif, we first have to fit a model. However, the data is $p < n$$p > n$. We cannot fit a linear regression model with OLS. Then we try to fit in LASSO but all coefficients are 0. Hence, I cannot check the multicollinearity.

Is there a way to check multicollinearity without a model? For example, correlation matrix. But what if $x1 = x2 + x3$?

I have read this post . I am not clear how does PCA detect (rather than handling) multicollinearity.

To check the multicollinearity, the method I know is to use vif in car library. To use vif, we first have to fit a model. However, the data is $p < n$. We cannot fit a linear regression model with OLS. Then we try to fit in LASSO but all coefficients are 0. Hence, I cannot check the multicollinearity.

Is there a way to check multicollinearity without a model? For example, correlation matrix. But what if $x1 = x2 + x3$?

I have read this post . I am not clear how does PCA detect (rather than handling) multicollinearity.

To check the multicollinearity, the method I know is to use vif in car library. To use vif, we first have to fit a model. However, the data is $p > n$. We cannot fit a linear regression model with OLS. Then we try to fit in LASSO but all coefficients are 0. Hence, I cannot check the multicollinearity.

Is there a way to check multicollinearity without a model? For example, correlation matrix. But what if $x1 = x2 + x3$?

I have read this post . I am not clear how does PCA detect (rather than handling) multicollinearity.

Source Link
Jill Clover
Jill Clover
  • 645
  • 2
  • 7
  • 17

Check multicollinearity for $p > n$ data

To check the multicollinearity, the method I know is to use vif in car library. To use vif, we first have to fit a model. However, the data is $p < n$. We cannot fit a linear regression model with OLS. Then we try to fit in LASSO but all coefficients are 0. Hence, I cannot check the multicollinearity.

Is there a way to check multicollinearity without a model? For example, correlation matrix. But what if $x1 = x2 + x3$?

I have read this post . I am not clear how does PCA detect (rather than handling) multicollinearity.