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We built two OnPLS models, one based on all variables (genes, proteins and metabolites) and one based only on the targeted variables (genes with corresponding proteins, and identified metabolites).
The alternative algorithm (implemented in function optimal.blcok) sought to first block specified variables (e.g., list of variables of primary interests) to generate a pool of assignment setups, then select the optimal one which minimize the objective functions based on all variables considered (including those variables which are not included in the block randomization step).
The imputed models were based on all variables already described plus country of residence (England, Wales, Scotland) and marital status.
Single imputations used the independent variables in the regression in question, while multiple imputations were based on all variables.
The separation is based on all variables analysed, i.e. breast milk levels of fatty acids, lactose and protein, serum phospholipids fatty acids and dietary variables.
The optimal one will be chosen by selecting the setup (from the pool generated in the block randomization step) which minimizes the value of the objective function based on all variables considered.
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The sequence alignment and the most parsimonious phylogenetic tree based on all variable positions support these relationships and place the Gommadie and Kaye Murri ecotypes, which both have a semi-compact panicle, to the same group (Additional file 2: Figure S7).
We note that CART differs with RF in that it only builds one tree based on all predictor variables; while RF combines multiple (1000 in our case) trees that are constructed on randomly selected predictor variables.
Imputation was based on all included variables, additionally including sample and the outcome variable of death and the cumulative hazard.
Additionally, we only show results for the PCAs based on all the variables; the results were essentially identical when only the significantly loading variables were included (data not shown).
The index uses principal components to build single indicators for wealth based on all the variables in DHS data that measure aspects of wealth [ 26].
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