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The purpose of the method was to minimize the sample dataset size to map soil depth to petrocalcic horizon based on ordinary cokriging, five calibration sample sizes (returned by Conditioned Latin hypercube –cLHS-), and apparent electrical conductivity (ECa) or elevation as variables of auxiliary information.
This information is employed by PAM-SLIM to compose the sample dataset to cluster the data.
where and are the estimates of the mean absolute value and the variance of the sample dataset, respectively.
It is important to note that the quality of the sample dataset is affected by the features of the tree regarding the distribution of the elements on the tree nodes.
Parallel analysis works by generating many random parallel datasets, with the same number of variables and cases as the sample dataset; each of these parallel datasets, which are filled with independent randomly generated data, is analyzed using principal component analysis.
Given the sample dataset X = [x1, x2, …, x M ], x i ∈ ℝ N, we convert the real data matrix X ∈ ℝN × M to a complex matrix Z ∈ ℂN × M by the mapping (8) and perform matrix factorization in this complex feature space.
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QS performed the case studies with the sample datasets.
Assume that there are N aligned reads in both the sample datasets.
Second, algorithms and methodologies such as k-means clustering and an adapted Finkelstein Schafer statistical method were implemented to identify representative HVAC operating conditions from the sampled dataset.
The sampled dataset is then passed on a groupReduce transformation, grouped by a shift number.
The sampled dataset was constructed by using maximum Spearman rank-correlation coefficients to map the best matching ring, trophozoite, young schizont and mature schizont time points between the original P. falciparum and P. berghei datasets.
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