Logistic regression results between main principal components (PCs) and the invasion status (all coefficients were statistically significant at P < 0.001)
| Parameter . | Logistic regression coefficient . | ||
|---|---|---|---|
| . | PC1 . | PC2 . | PC3 . |
| β | 0.222 | 0.301 | −0.939 |
| Exp(β) | 1.249 | 1.351 | 0.391 |
| Parameter . | Logistic regression coefficient . | ||
|---|---|---|---|
| . | PC1 . | PC2 . | PC3 . |
| β | 0.222 | 0.301 | −0.939 |
| Exp(β) | 1.249 | 1.351 | 0.391 |
When β > 0, the component was positively related to the invasion, while the component was negatively related when the β value < 0. Exp(β) represents the multiple of the probability of invasion for every 1 increase in the value of the variable.
Logistic regression results between main principal components (PCs) and the invasion status (all coefficients were statistically significant at P < 0.001)
| Parameter . | Logistic regression coefficient . | ||
|---|---|---|---|
| . | PC1 . | PC2 . | PC3 . |
| β | 0.222 | 0.301 | −0.939 |
| Exp(β) | 1.249 | 1.351 | 0.391 |
| Parameter . | Logistic regression coefficient . | ||
|---|---|---|---|
| . | PC1 . | PC2 . | PC3 . |
| β | 0.222 | 0.301 | −0.939 |
| Exp(β) | 1.249 | 1.351 | 0.391 |
When β > 0, the component was positively related to the invasion, while the component was negatively related when the β value < 0. Exp(β) represents the multiple of the probability of invasion for every 1 increase in the value of the variable.
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