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A bivariate correlated factor model revealed two dimensions with a genetic correlation of −.57 (CI −.67, −.47), while the non-shared environmental correlation was estimated to be −.21 (CI −.25, −.16).
The correlated factor solution is the least restricted model allowing variables to correlate with one another via genetic, shared environment, and non-shared environment.
The model results were consistent with field observations, and the slope was the most strongly correlated factor with soil type of the nine environmental variables.
As it is difficult to accurately capture the effects of various damage-related phenomena through analytical models due to intricacies of the problem, it is plausible and perhaps more encouraging to seeking an experimentally correlated factor that collectively and meaningfully represents the adverse effects of corrosion other than the metal loss.
PC1 was positively correlated (factor loadings >0.640) with the latency measures (latency to move, feed, walls), but not with T walls), CeilingJumps, or PositionChanges (factor loadings <0.220).
INF remained an independent correlated factor according to multivariate analysis.
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If some factors were found to be correlated with other factors, the correlated factors were not analyzed simultaneously because of multicollinearity.
Most items did, however, load as expected on their predicted dimensions (see Table 1) and the factor correlations support the assumption of correlated factors.
This model tests the extent to which different measures can be grouped into separate yet correlated factors, and examines the latent correlations among the factors.
They found that sleep and appetite/weight is the pair of less correlated factors and sleep variability is inversely correlated with irregularity of data.
If factors were highly correlated, we selected only one of the correlated factors in the multivariate model to avoid the problem of co-linearity.
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