Computing partial (conditional) correlation

It is not necessary to only look at the absolute value of CPC. In summary, it is acceptable to use absolute pearson correlations in the computation of conditional pearson correlation (CPC) and the assumption that a more negative CPC indicates less dependency on the third variable is correct.
  • #1
pegahtv
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0
I wanted to compute the conditional pearson correlation (CPC). First, I wanted to make sure if it is OK to use the absolute pearson correlations in the computation of CPC. Becuase I think only the strength of pairwise correlations should be considered in computing CPC, and not the sign. Second, I am supposing that the more the CPC negative, the less the dependency of the two variables on the third variable and vs.. Is this assumption correct, or should I only look at absolute value of CPC?

thanks in advance for your answer.
 
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  • #2
Yes, it is ok to use absolute pearson correlations in the computation of CPC. The strength of the pairwise correlations should be considered, but the sign can be ignored. Your assumption is correct - if the CPC is negative, then the two variables are less dependent on the third variable, and vice versa.
 

FAQ: Computing partial (conditional) correlation

1. What is partial (conditional) correlation and why is it important in computing?

Partial (conditional) correlation is a statistical measure that determines the relationship between two variables while controlling for the effects of one or more additional variables. It is important in computing because it allows us to understand the unique relationship between two variables, without the influence of other variables.

2. How is partial (conditional) correlation different from regular correlation?

Regular correlation measures the strength and direction of the relationship between two variables, but it does not take into account the effects of other variables. Partial (conditional) correlation, on the other hand, controls for the effects of other variables to determine the unique relationship between two variables.

3. What is the formula for computing partial (conditional) correlation?

The formula for computing partial (conditional) correlation is rxy.z = (rxy - rxz * ryz) / √(1 - rxz^2) * √(1 - ryz^2), where rxy.z is the partial correlation between variables x and y, controlling for variable z.

4. How is partial (conditional) correlation used in machine learning and data analysis?

Partial (conditional) correlation is commonly used in machine learning and data analysis to identify and understand the relationships between variables, while controlling for the effects of other variables. It is especially useful in identifying the most important variables in a dataset and in developing predictive models.

5. Are there any limitations to using partial (conditional) correlation?

Yes, there are some limitations to using partial (conditional) correlation. It assumes a linear relationship between variables and can only capture the linear associations between variables. It is also sensitive to outliers and can be affected by the number of variables included in the analysis. Additionally, it cannot determine causality, only associations between variables.

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