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We created 95% confidence limit intervals using the percentile bootstrap.
Confidence intervals were derived using the percentile bootstrap method with 200 replicates.
Ninety-five perconfidencedence intervals (CIs) of DI were determined by using the percentile bootstrap method after 1,000 replicates implemented in the package boot, written by A. Canty and B. Ripley for R (R Foundation for Statistical Computing, Vienna, Austria), version 2.8.0 (www.r-project.org).org
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To generate p-values that quantify the depression-related shift in gene coordination, we used the percentile bootstrap method.
Because gene coordination relies on multiple samples to form a single measure, we used the percentile bootstrap method to ensure that the shifts were robust and significant (p<0.05) and then applied the Benjamini-Hochberg FDR [38]. Benjamini-Hochberg FDR [38]
We used the percentile bootstrap [ 39] on all plots within each community to estimate the 95% confidence limit intervals for the NNIs.
We used the percentile bootstrap method (3000 bootstrap re-samples) to obtain 95%% confidence intervals (CI) and considered results to be statistically significant at the 0.05 level if the 95 % CI for measures of association excluded the null value.
For internal validation of the statistically significant models, we used the percentile bootstrap method, with 1000 resampling simulations, with the aid of the "boot" package of R. Data are shown as mean ± standard deviation for normally distributed continuous variables and as relative or absolute frequencies for categorical variables.
To express the degree of uncertainty around ICERs, we present 95% CIs, using the percentile method based on bootstrap replicates (20).
Bias-corrected uncertainty intervals (UI) of costs have been calculated using the percentile method of nonparametric bootstrapping (using 1000 replications).
Confidence intervals (CIs) were calculated using the percentile bias corrected and accelerated bootstrap method.
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