Batrachomatus daemeli, which was split into three entities, is a more complex example, with samples from localities NSW112 and VIC120 included in one entity, and one from each NSW82 and VIC120 in two additional entities (distance NSW82-NSW112 c. 620 km, NSW112-VIC120 c. 400 km).
Table 2 shows the margins of error for a prevalence of 10% (taken as an example) associated with sample sizes ranging from 200 to 10,000.
For example, with a sample volume of 0.1 m the power to detect a moderate exceedance (14·m−3 or 40% above the IMO standard) is very low (∼0.05).
This is illustrated by the posterior distributions of DS and DN obtained for this particular example with a sample of 500 individuals (Fig. 4).
For example with a sample size of 600, the bias of the SEM was -0.6% while the bias for the unscaled NLMM was -3.8% and the bias of the scaled NLMM was -1.6%.
As an example, with a sample size of 1600 and the penetrance function shown in Table 1 the sizes of these sets are (1) For the analogous sets involving hits we have (2) These are actually expected numbers rounded to the nearest integer.
Formally, the template graph of D (denoted by T = (V, E T, ϕ is an edge weighted graph where E T is the set of all edges in D, ϕ : E T → ℝ and ϕ (e ) = - log (f r (e, D ) ), ∀ e ∈ E T. Figure 2 shows an example with a sample collection of graphs and clarify the idea presented above.
The same strategy was adopted for the archival tag example, with 30,000 samples drawn for burn-in, and a further 800,000 samples drawn.
For example, with patient samples IROC008 and IROC036, ascites fluid enhanced both CD8+ and CD4+ T-cell proliferation but inhibited expression of CD107a, CCL4, and IFN-γ (Fig. 1A, and data not shown).
For example, with 20 30 samples with coefficients of 20%% in metabolite concentrations and 50%% in reaction rate estimates, estimates have a relative standard error of less than 20%%.
