You can install the development version of fullRankMatrix fromGitHubwith: #install.packages("devtools")devtools::install_github("Pweidemueller/fullRankMatrix") Example When using linear models you should check if any of the columns in your model matrix are linearly dependent. If they are this wi...
The present note shows that balanced uniform repeated measurements designs fulfill this criterion.doi:10.1007/BF02613152J. KunertPhysica-VerlagMetrikaAn Example of Universal Optimality in a Full-Rank Model - Kunert - 1987
k and d is a full-rank matrix, an infinite number of solutions are available for the above representation problem. thus, a new constraint is introduced into this problem, and the solution is obtained by solving min x y - dx 2 2 subject to | | x | | 0 ≤ t , (1) where ||·|...
(Note that, by condition (iii), the matrix G is assumed to have maximal rank and hence any column of the matrix T is equal to GpT, for some word p.) We will illustrate the concept superduality by an example. Example 3 We consider the couple (G∣T) below: In this case n=15, ...
where T∈RN×d is the score matrix, P∈RD×d is the loading matrix, d is the retained latent dimensionality, and E is the residual matrix. Technically, if we consider the covariance as the example, by performing the Eigen-decomposition of the covariance matrix S=(XTX)/(N−1), we ge...
Let's construct the design matrix: ThemeCopy X = [dummyvar(tab.A1), dummyvar(tab.A2)]; % DummyVarCoding -> full disp(rank(X)) % 3 < size(X, 2) --> 3 < 4 --> rank deficient 3 % what about when considering them alone? disp(rank(X(:...
The decomposition of the data matrix into loading vectors and latent variables provide valuable graphical outputs to easily visualize the results. For example, the latent variables can be used to represent the similarities and dissimilarities between the samples: Figure 4 illustrates the difference in ...
Second to last of tested ranking metric is the fold change rank ordering statistics (FCROS), which is based on a truncated mean calculated from the matrix of fold changes from pairwise comparison between sample groups [40]. Finally, we used the Minimum Significant Difference (MSD) [41] that...
Identifying pathogenic variants from the vast majority of nucleotide variation remains a challenge. We present a method named Multimodal Annotation Generated Pathogenic Impact Evaluator (MAGPIE) that predicts the pathogenicity of multi-type variants. MAG
[224], featured by the application of personalized PageRank algorithm, which was used to calculate ligand–target regulatory potential scores [228]. Various types of machine learning algorithms are adopted in the machine learning-based approaches, such as SingleCellSignalR [218], similarity matrix-...