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Bayesian partial correlations #301
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Parameter1 | Parameter2 | rho | 95% CI | pd | % in ROPE | Prior | BF
Sepal.Length | Sepal.Width | -0.11 | [-0.27, 0.04] | 91.62% | 44.38% | Beta (3 +- 3) | 0.509
Sepal.Length | Petal.Length | 0.86 | [ 0.82, 0.90] | 100%*** | 0% | Beta (3 +- 3) | 2.14e+43***
Sepal.Width | Petal.Length | -0.41 | [-0.54, -0.28] | 100%*** | 0% | Beta (3 +- 3) | 3.49e+05***
My aim is to compute bayesian partial correlations. I tried 2 different codes:
Which one yields the most reliable results?
Correlation Matrix (pearson-method)
Parameter1 | Parameter2 | rho | 95% CI | pd | % in ROPE | Prior | BF
Sepal.Length | Sepal.Width | -0.11 | [-0.25, 0.05] | 91.42% | 44.55% | Beta (3 +- 3) | 0.509
Sepal.Length | Petal.Length | 0.86 | [ 0.82, 0.90] | 100%*** | 0% | Beta (3 +- 3) | 2.14e+43***
Sepal.Width | Petal.Length | -0.41 | [-0.55, -0.28] | 100%*** | 0% | Beta (3 +- 3) | 3.49e+05***
Observations: 150
Correlation Matrix (pearson-method)
Parameter1 | Parameter2 | rho | 95% CI | pd | % in ROPE | Prior | BF
Sepal.Length | Sepal.Width | 0.57 | [ 0.45, 0.66] | 100%*** | 0% | Beta (3 +- 3) | 6.96e+11***
Sepal.Length | Petal.Length | 0.91 | [ 0.87, 0.93] | 100%*** | 0% | Beta (3 +- 3) | 2.81e+55***
Sepal.Width | Petal.Length | -0.65 | [-0.74, -0.56] | 100%*** | 0% | Beta (3 +- 3) | 4.73e+17***
Observations: 150
2.a) How does it works when I have 4 or a greater number of variables?
2.b) Would it be possible to determine which variable (either "Petal.Length" or "Petal.Width") controls the correlation between "Sepal.Length" | Sepal.Width"?
g<-correlation(iris, select=c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width"), bayesian = TRUE)
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