Sentence examples for multivariate classification analyses from inspiring English sources

Prior to multivariate classification analyses, values were transformed into log value.

After 13 proteins were selected as candidate biomarkers, multivariate classification analyses were used to identify algorithms for diagnostic biomarker combinations.

To investigate the neural basis of VSTM for contrast, we have recently begun to use a combination of fMRI and multivariate classification analyses to look at early visual cortex (Xing, Ledgeway, McGraw, & Schluppeck, 2013, see also Ester et al., 2009; Harrison & Tong, 2009; Sneve et al., 2012).

Novel multivariate pattern classification analyses have enabled the prediction of decision outcomes from brain activity prior to decision-makers' reported awareness.

Among multivariate statistical analyses, the classification tree analysis is suggested to be superior to cluster analysis or the logistic regression analysis in identifying distinctive homogeneous subgroups for further development of tailored intervention [ 34].

All the multivariate statistics analyses of lithofacies classification and permeability modeling with results visualizations were implemented through R, the most powerful open-source statistical computing language.

Because in Germany, the use of drugs with the Anatomical Therapeutic Chemical (ATC) code N06BA is legally approved from the age of 6 years on, for the bivariate and multivariate analyses a classification in the age groups 0 5, 6 10, 11 13 and 14 17 years was chosen.

These univariate and multivariate Cox PH analyses suggest that the classification of tumours using biclustering can, alone or in combination with other clinical factors, be a significant prognostic variable, especially in pathology-specific patient subsets, in comparison to conventional clinical-pathological factors.

Specifically, for T-classification and N-classification categories, the proposed staging criteria showed to be significant predictor for FFS in univariate and multivariate analyses (T-classification: the log-rank test Ptrend<0.001; adjusted HR 1.63, 95% CI 1.38 1.92, P<0.001; N-classification: the log-rank test Ptrend<0.001; adjusted HR 1.72, 95% CI 1.47 2.02, P<0.001).

On the contrary, no significant increases in the HRs of tumour recurrence or death were observed in the pTNM, NPI, modified NPI, and HG classifications in the multivariate analyses.

As T-maps combine the effect size weighted by error variance for a modeled response, they provide high-classification accuracy in multivariate analyses since results are not unduly influenced by large, but highly variable response estimates (Misaki et al. 2010).