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A model assuming correlated evolution between the two characters was preferred over independent evolution of host-searching niche and body shape.
To overcome the possible bias of a priori grouping of individuals into taxa or differentiated populations, we used the Bayesian approach implemented in STRUCTURE v.2.3.4 (Pritchard et al. 2000), using an admixture model assuming correlated allele frequencies.
This finding is often used to justify the use of a multilevel model, assuming correlated observations, per hospital, over time.
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The model furthermore assumes correlated normally distributed random effects between studies.
Because individuals may have mixed ancestry, we applied the admixture model that assumes correlated allele frequency, in which each individual draws some fraction of his/her genome from each of the K populations.
The model used in the simulations assumed correlated allele frequencies within populations, and admixture allowed for mixed ancestry within individuals.
Methods 1 to 3 assume that the genotypes are not correlated, while Methods 4 and 5 assume correlated effects according to the model underlying the RR-BLUP.
We assumed correlated, but independent latent variables within our models corresponding to former research and with respect to varying correlations among HLOC scales reported in different studies [e.g. [ 15, 29]].
Specifically, they involve dividing predictive ability computed from an RR-BLUP model, assuming that genotypes are correlated, by the square root of heritability computed from a model assuming that genotypes are uncorrelated.
The computation of predictive ability using the model assuming that the genotypes are correlated, when heritability is computed assuming uncorrelated genotypes, would seem unavoidable when using RR-BLUP.
We estimate the above equation with a random-effect IV model, assuming unobservable heterogeneity c age i is not correlated with the regressors.
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