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For predictor variables we used items from the baseline ASI, the baseline UDS and CSSA scores obtained at baseline.
We used 95% confidence intervals at each time point to estimate if the betas for predictor variables were significantly different from chance.
Table 1 Descriptive statistics for predictor variables Mean Std.
Similar to this, the Mann–Whitney test (U) was used for predictor variables with two categories.
These latter studies all employed active sensor (radar or ALS) imagery as the single source for predictor variables.
For predictor variables, thin-plate splines were used, initiated with a dimensional basis of two, thus ensuring simple response curves.
Bayesian mixture models are the most common algorithms for predictor design (see Bayesian mixture models and redundancy-capacity theorem for optimality analysis [20,23,28,31]).
Use of transformations for predictor variables has been examined in nonlinear models for improving model performance (Wang et al. 2007; Timilsina and Staudhammer 2012).
For example, a set of finite kth-order Markov models are more practical for predictor design compared to the set of all arbitrary order Markov models.
This works the same as before but new parameters have been added to allow you to specify constant values for predictor variables that do not vary spatially and therefore do not have rasters representing them.
We then conducted large-scale Monte Carlo studies, searching for predictor weights that also lead to these donor weights in the training period, i.e., "training-equivalent" predictor weights which also minimize Eq. (2).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com