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The variation of the energy influx with the RF power was found to follow a linear profile and a simple relation is proposed to model this variation for the considered plasma.
To model this variation we fitted a generalised additive model to the reporting rate as a function of week number (1 7 Jan = 1 to 25 31 Dec = 52) in the form of a thin-plate regression spline using gamm with a binomial error structure and logit link function [50].
Analysis methods need to be able to cope with and appropriately model this variation.
The lack of data on diagnostic performance in different settings limits our ability to adequately model this variation, although it could theoretically be incorporated into these types of simulation models.
Assuming that countries with larger variation in mortality rates between their internal regions indicate greater inequalities than countries with smaller variation, we model this variation by relating it to the distribution of region populations within each country.
When proportion of health facility delivery is less than 50%, the effect size becomes 0.74 (95% CI: 0.59, 0.94), when the proportion of health facility delivery is 50% or above the effect size becomes 0.61 (95% CI: 0.43, 0.87) in the random effects model, however, in the fixed effects model this variation is much more significant (Table 2).
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Recent advances in measurement techniques (e.g. Riddell et al. 2012) now mean that it is possible to collect higher resolution data on the variation in SGA within a stem, particularly in the circumferential direction, which will enable a better understanding of the patterns of variation as well as providing better data for modelling this variation.
We have statistically modelled this variation in the soil faunal biomass distribution and biomass flux; showed that occurrence of livestock is a reliable allometric predictor; and assessed that cattle manure enhances lower body-size clumps in the faunal biomass distribution.
In the adjusted models, this variation reduced to 0.44 and 0.12, respectively.
Although all four methods described above model for this variation, variation of synonymous rates from codon to codon in each dataset was tested with the dNdSRateAnalysis.bf batch file in HyPHy, as described elsewhere [92].
While we here simply model this fitness variation as arising from circulating deleterious mutations, any of these non-antigenic phenotypes can similarly contribute to this fitness variation.
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