Table 1. Results of the GAM fitted for each response variable.
| Models for R0 | loglik | sum edf | BIC | BICw | R2 |
|---|---|---|---|---|---|
| Ln (R0) = 6.48 + s1(D, df = 1.11) − 0.02 × Tm + 0.32 × Tv − 0.02 × Tm × Tv | −463,40 | 5,11 | 952,94 | 0,16 | 0,43 |
| Ln (R0) = 7.15 + s1(D, df = 1.10) − 0.05 × Tm − 0.03 × Tv | −469,90 | 4,10 | 961,64 | 0,00 | 0,33 |
| Ln (R0) = 7.23 + s1(D, by = Tv, df = 0.46) − 0.06 × Tm + 0.02 × Tv | −475,60 | 3,46 | 970,35 | 0,00 | 0,22 |
| Ln (R0) = 7.18 +s1(D, by = Tm, df = 1.02) − 0.05 × Tm + 0.03 × Tv | −469,00 | 4,02 | 959,58 | 0,01 | 0,35 |
| Ln (R0) = 6.50 + s1(D, by = Tm, df = 1.03) − 0.01 × Tm + 0.32 × Tv − 0.02 × Tm × Tv | −462,20 | 5,03 | 950,68 | 0,51 | 0,45 |
| Ln (R0) = 6.51 + s1(D, by = Tv, df = 0.22) − 0.02 × Tm + 0.34 × Tv − 0.02 × Tm × Tv | −470,10 | 4,22 | 962,56 | 0,00 | 0,33 |
| Ln (R0) = 6.51 +s1(D, by = Tm, df = 1.15) + s2(D, by = Tv, df = 0.68) − 0.01 × Tm + 0.31 × Tv − 0.02 × Tm × Tv | −461,20 | 5,83 | 951,66 | 0,31 | 0,46 |
| Ln (R0) = 7.19 + s1(D, by = Tm, df = 1.15) + s2(D, by = Tv, df = 0.58) − 0.05 × Tm − 0.04 × Tv | −468,10 | 4,72 | 960,77 | 0,00 | 0,36 |
| Ln (R0) = 7.07 + s1(D, by = Tm, df = 2.78) + s2(D, by = Tv, df = 1.79) | −467,90 | 5,56 | 964,03 | 0,00 | 0,35 |
| Ln (R0) = 7.06 + s1(D, by = Tm, df = 2.83) | −471,00 | 3,83 | 962,73 | 0,00 | 0,31 |
*D is population density, Tm is mean temperature, and Tv is thermal variability. sirepresents the cubic regression spline for this variables and df are the effective degrees of freedom for each term. Loglik is log likelihood values, sum edf is the sum of effective degrees of freedom, BIC is the Bayesian information criterion for the model, BICw is the weight of this model (see methods), and R2 is the determination coefficient. Note that GAM BIC is calculated using the sum of the edf as an equivalent to the traditional number of parameters.
*D is population density, Tm is mean temperature, and Tv is thermal variability. sirepresents the cubic regression spline for this variables and df are the effective degrees of freedom for each term. Loglik is log likelihood values, sum edf is the sum of effective degrees of freedom, BIC is the Bayesian information criterion for the model, BICw is the weight of this model (see methods), and R2 is the determination coefficient. Note that GAM BIC is calculated using the sum of the edf as an equivalent to the traditional number of parameters.
*D is population density, Tm is mean temperature, and Tv is thermal variability. sirepresents the cubic regression spline for this variables and df are the effective degrees of freedom for each term. Loglik is log likelihood values, sum edf is the sum of effective degrees of freedom, BIC is the Bayesian information criterion for the model, BICw is the weight of this model (see methods), and R2 is the determination coefficient. Note that GAM BIC is calculated using the sum of the edf as an equivalent to the traditional number of parameters.