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Principal component analysis (PCA) was used to determine among the different landscape variables the one that could best explain the data variance, and the results were tested against canine seroprevalence in order to address disease occurrence risk levels.
LDA looks for low-dimensional linear combinations of variables, which best explain the data, by maximizing the between-class scatter matrix while minimizing the within-class scatter matrix and form a linear discriminant function resulting in least misjudgments [87, 88, 89].
AIC and BIC measure the goodness of fit of an estimated model and favor models that best explain the data using the fewest free parameters.
However, arguably, one would hope that the selection procedure would select the variables that do indeed best explain the data.
The number of ancestral populations that best explain the data is one of the parameters of our model.
We use a Gibbs sampler to perform inference on the resulting model and infer the ancestry proportions and the number of clusters that best explain the data.
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The model that best explained the data consisted of two "gay genes," with at least one on the X chromosome.
We then compute the optimal FT for the recalibrated sample spectrum and the candidate molecular formula, and use this score to evaluate which root molecular formula best explains the data.
A sequential comparison of the models using chi-square difference tests (e.g., χ2 = 16.26, df = 2, p < .001; see the third row in Model fit in Table 8) shows that Model 3 best explained the data.
Online today in Nature, the team reports that the models that best explained the data were those that assumed the killings were related to adaptive strategies, which in statistical terms were nearly seven times as strongly supported as models that assumed human impacts were mostly responsible.
To make sure whether the optimum model best explained the data, goodness of fit test statistics using pseudo R 2, Akaike Information Criteria (AIC), and Bayesian Information Criteria (BIC) were used, while measures of association were checked through likelihood ratio (Campbell-Smith et al. 2010).
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