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As the dependent variable of this analysis is binomial (i.e. a seedling bolts or not), this statistical modelling assumed binomial error and a logit link function.
In other models, the model assuming equal variance between sexes fitted the data best.
For this simulation, we used a classical Wright-Fischer model assuming a binomial law to draw individuals from one generation to the next.
Second, means were smoothed by a hierarchical model assuming their variances (improved estimated variances) are known.
To explain the data with a logistic regression model that assumes binomial distributions, additional risk factors need to be invoked.
Following previous studies [ 47] we do not report log likelihood support values for our phenotype data as they may violate ClineFit's likelihood model assuming a genetic model with binomial variance.
The model assumes homogeneous variance (ie, τ jk = τ).
The model assumes a binomial distribution of the within-study variability (variability between sensitivity and specificity within a study).
The model assumed a binomial error distribution with regression parameters calculated by maximum likelihood.
The model assumes a binomial distribution that is more realistic than a Gaussian used by some implementations of HetROC.
For comparison we show the same graph using a binomial model (assuming independence between individuals regarding FIV, Fig. 6b).
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