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Finally, the importance of each variable to the final model was analyzed using the jackknife procedure implemented in Maxent, where separate models are analyzed that include each variable alone; the resulting effect on training gain can be viewed as a measure of the information contained by the particular variable.
Table 2 lists the average errors between predicted and actual MLI and average errors of the absolute value in our model using the jackknife method and Mito et al.'s method (MM).
Given low occurrence data samples, we tested model predictions using the jackknife manipulation proposed by Pearson et al. [71], the only robust test for evaluating models based on small samples: N−1 points are used to develop N jackknifed models.
We proceeded to fit several models in sequential steps using the jackknife procedure in Maxent, each time removing variable(s) that were weak contributors to the overall quality (gain) of the respective model, or that did not make significant independent contributions to the model.
The relative contribution of each environmental dimension to the overall predictive model of human monkeypox occurrence was assessed using the jackknife procedure ( Table 1 ).
Quite promising predictions are obtained using the jackknife test and compared with other predictive methods.
Heltshe, J. F. & Forrester, N. E. Estimating species richness using the jackknife procedure.
Means and standard errors were estimated using the jackknife procedure.
Confidence intervals were derived using the jackknife method.
The estimate of the precision of the SRM calculated was performed using the Jackknife technique; 95% CI Jackknife SRMs are given in Table 6.
We ran LDN e using a model of random mating and used the jackknife option to construct 95% confidence intervals.
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