The variance covariance matrix Σ of univariate test statistics using the sample covariance matrix of the test statistics of all SNPs from univariate GWAS analyses as an approximation.
where f(S, T 1 ‒ 4 is the distance function of the corresponding feature 1-4, s i,j is the feature matrix of the prototype class (S), and t i,j is the feature matrix of the test sample (T).
The V matrix that was extracted from the reference dataset was also used to construct the T matrix for the test dataset as Tt = Xt V, where Xt contains the genotypes of the test dataset.
where n test is the number of test objects in the outer loop, X test,k is the predictor matrix of the test objects in the kth outer loop iteration.
From the Evince confusion matrix for the test set, it was calculated that for F. subglutinans 98.6%% of the pixels were correctly classified (Table 2).
> From the Evince confusion matrix for the test set, it was calculated that for F. subglutinans 99.9%% of the pixels were correctly classified with 0.1 % pixels not classified.
The most suitable water matrix for the test material preparation was selected according to its similarity to natural waters while taking into account several practical aspects.
The cost matrices of the test problems are given in Tables 5 and 6.
In Tables 8, 9 and 10, the related confusion matrices of the test results are given for 40-, 80- and 120-s-long samples, respectively.
Note that the rank of the matrix B in the test nonsymmetric saddle-point matrix is (m-1), which means that the discretized nonsymmetric saddle-point matrix (mathcal{A}) is singular.
