Table 1. Regression results for the best (lowest AIC) regression model.
Estimate | Std. Error | t value | Pr(>|t|) | Bootstrap estimated P | |
---|---|---|---|---|---|
(Intercept) | 0.000 | 0.006 | 0.000 | 1.000000 | 0.521 |
MilTech | 1.043 | 0.025 | 42.114 | < 2e-16 | 0.000 |
MilTech.sq | -0.175 | 0.026 | -6.862 | 1.12e-11 | 0.000 |
IronCav | 0.047 | 0.012 | 3.973 | 0.000076 | 0.000 |
Agri | 0.020 | 0.008 | 2.542 | 0.011 | 0.028 |
WorldPop | 0.039 | 0.011 | 3.505 | 0.00047 | 0.001 |
Centrality | 0.027 | 0.008 | 3.375 | 0.00076 | 0.000 |
Phylogeny | 0.037 | 0.008 | 4.486 | 8.01e-06 | 0.005 |
Estimate shows the standardized regression coefficients, which provide a direct measure of relative effects by the lagged predictors on the response variable. Thus, MilTech here represents the linear autoregressive term, AR(1). The column “t value” lists t-statistics, a measure of statistical significance of regression terms associated with various predictors. Pr(>|t|) is the statistical significance for regression assuming the Normal distribution of residuals, while Bootstrap estimated P is the result of nonparametric bootstrap that does not make this assumption.