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There are two basic kinds of context biases: those based on frequency and those based on who is modeling the trait.
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It is surprising that much effort has been dedicated to issues like data normalization [ 3] or computing efficiency [ 4] whereas modelling the trait itself (i.e., the expression level) has been severely neglected.
Percent phenotypic variance attributable to a locus was estimated by modeling the binary trait as continuous.
Because we have modeled the cultural trait after a microsatellite locus, we can use HT to quantify cultural diversity in our artificial populations.
In particular, by directly modeling the heritability of the latent traits, we can predict their evolution.
In multivariate linear regression models, the trait (index of insulin sensitivity/secretion) was chosen as dependent variable.
In these models the trait was the dependent variable whereas age, sex, insulin sensitivity and genotype were the independent variables.
In contrast, explicit quantitative genetic modeling of the trait leads to insights regarding the effect of the trait architecture on the allele frequency trajectory of a QTL.
Mr. Humm and Mr. Guidara like to say that they have modeled the restaurant on traits they associate with Miles Davis.
Model 4, the final model, allows the trait factors to correlate freely while the method factors are constrained to be perfectly correlated with each other.
In our model, the traits examined do indeed influence predation rate in opposition, but their combined influence on community structure is mechanism- and context-dependent.
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