In a non-linear framework, however, the underlying latent outcome variable has a scale that is unknown and depends on the predictors included in the model, and thus calculating indirect effects in nonlinear models using techniques developed for linear regression conflates rescaling with mediation (Kohler, Karlson, & Holm, 2011; Mood, 2010).
To visualize the estimate risks depending on the predictors, nomograms were displayed.
Ultimately, the sequential predictor performance depends on the predictor set (mathbb {M}) "complexity" or richness, which quantifies the class type, size, and statistical regression between observations [19,20,23,29].
However, the sequential predictor performance generally depends on the predictor set (mathbb {M}) class "complexity" or richness, which quantifies the class type, size, and statistical regression between observations [19,20,23,29].
Therefore, depending on the predictor, H P, i is a similarity matrix based on either distinct observed haplotypes (e.g. Score) or distinct clusters (e.g. P = Beagle).
For example, Wilby and Wigley (2000) find that optimum predictor domains for precipitation downscaling are not necessarily located immediately above the target location, depending on the predictor variables applied.
Depending on the predictor set PBMS explained between 41 and 50% at baseline and between 69 and 78% of the variance after educational programme in the total sample (Table 2).
Because the covariance parameters of the stochastic component depend on the selected predictor variables and vice versa, an iterative approach was used.
