Both models were compared regarding the degree of variability explained by each variable.
Overall, each successive model was an improvement over the last, both in terms of variability explained and added variables being significant improvements.
In each model, we studied the degree of variability explained by each variable by means of the R-square and -2 log L statistics, respectively.
The column in between (R) represents the proportion of variability explained in a particular outcome variable by its set of predictor variables.
This allowed us to assess the amount of variability explained by a single variable [42].
The significance of cancer-specific expression differences was obtained from an F-test of the variability explained by the cancer/normal variable.
This amount of variability explained by the group-level variable is higher than that reported in other studies.
Hence the variability explained by these three fixed variables is lower than the unexplained between-individual variability.
Fig. 2 Heatmap of leaf-out variability explained by phylogeny and 11 climate variables in the different families studied.
We also calculated the semi-partial correlations between these variables to estimate the percentage of CPR variability explained by both predictors after controlling for the others.
Table 5 gives percentages of practice-level variability explained by the patient- and practice-level predictor variables.
