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The procedure for implementing double BOOT is as follows: Perform steps 1 6 above of the BOOT method.
The BOOT method used by Martin and Roberts (2006) proceeds through the following steps: Fit the K candidate models defined by Model [1].
In the paragraphs that follow, we describe Akaike's information criterion (AIC; Akaike 1973) and outline the bootstrap (BOOT) method used by Martin and Roberts (2006) and our extension that refines this method.
A report of particular relevance to the present study is that of Buckland et al. (1997) who investigated various forms of bootstrap model averaging, including the BOOT method in the present investigation.
Click "File"> Edit Configuration File", click "Continue", and change "Boot Method" to auto.
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The 1-σ standard error distribution is obtained with a boot strapping method.
Model error is also estimated with a boot strapping method (e.g., Tichelaar and Ruff, 1989).
We estimated these 95% confidence intervals by random sampling with replacement using the boot strapping method implemented in the R statistical software package.
Mean laterality index (LI) values were determined for each individual and region of interest after sampling across 20 different activation thresholds using a boot strapping method (Wilke and Schmithorst, 2006).
Th cost-effectiveness of the treatment is shown in Table 5. Boot-strap method was used to estimate the robustness of these estimates.
We used the non-parametric boot-strap method to assess the normality of this ratio and then constructed the confidence intervals [ 6].
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