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One possibility would be to estimate the response propensities of respondents by logistic regression models and compute the weight adjustment factors based on these models (e.g., Lepidus Carlson and Williams 2001; Watson 2012).
All other computations were performed using freely available R software (http://www.r-project.org); computations associated with GenGR-GC were facilitated by GenABEL package for R [19], implementing procedures to compute genomic kinship, maximize polygenic models and compute the residuals.
To compute true- and false-positive rate, we iterate through the ranks of the generated similarity matrix (between drugs and mouse models) and compute, for each rank, the proportion of known drug targets in each of our evaluation datasets identified up to this rank (true-positive rate) as well as the proportion of targets not in the evaluation dataset included up to this rank (false-positive rate).
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The accuracy of the statistical models and computed estimates were validated in the following years.
Additionally, hydraulic models and computing power are now sufficient to cope with large (5,000,000 grid cells) areas of inundation and large (100,000 m3 s−1) peak discharges.
In addition, we also treated season-standardized 25OHD concentrations as a continuous variable in the regression models and computed the ORs and 95% CIs associated with an incremental increase of 10 ng/mL of 25OHD.
We calculated age-standardised admission rates per 100,000 person years by income, examined risk ratios using Poisson regression models and computed concentration indices separately for men and women.
To illustrate this interaction effect, we estimated the expected values from the hierarchical linear models, and computed separately three scenarios assuming a hypothetical increase of 10% in (1) knowledge, (2) attitudes and (3) self-efficacy in Table 4.
For each size, we randomly selected 100 samples, ran all five models, and computed all the statistical measures mentioned above (RMSE, MAPE, and PRs by decile of predicted cost).
To reduce problems of multiple testing, we used a significance level of 1%% in the GEE models and computed the 99 % confidence intervals, as other authors have done [ 8, 11].
In Section 4, we consider the stochastic CSI error model and compute the maximal achievable outage constrained secrecy rate.
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