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Fitted bi-dimensional cross-basis functions from DLNMs can be interpreted by deriving predictions over a grid of predictor and lag values, usually computed relative to a reference predictor value.
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For assessing the influence of local stand condition, past selective logging, and small-scale topographic position, we created a set of predictor variables by interpolating data recorded at the grid nodes, supplemented with elevation data at each tree.
For a large test bed of examples, the empirical mean squared prediction errors are compared at a grid of inputs for each test surface using a statistically calibrated Bayesian predictor based on the data from each design.
For a grid of λ and each bootstrap sample b, the functions of each predictor j are estimated as described above using the corresponding estimation set of each b.
The grid of two hundred million and the grid of intimacy.
No grid of lines was necessary.
The grid of the two hundred million and the grid of intimacy.
Grid of sink.
Hexagonal grid of columns.
HT colony grid fits a grid of fixed-size circles to the image.
Start laying the inside grid of squares.
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