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Subsequently, the clr-transformed data set was explored by biplots, a graphical representation of variables and cases projected on to principal component planes.
The multivariate character of seven dynamic soil properties from a national soil quality data set was explored to determine if generalizations can be made about the status of the properties from land use and soil order.
The entire HPLC-ESI-MS data set was explored by PCA.
As a first stage, the data set was explored using descriptive approaches.
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The influence of the possible selection of alternative data sets was explored in sensitivity analyses.
Thus, the data sets were explored using the statistical technique partial least squares regression (PLS), which is closely related to principal components analysis (PCA) [ 36, 37].
Microarray expression data sets were explored for the predicted AtLRR-RLK genes using the Arabidopsis Affymetrix GeneChip® average data available on the GENEVESTIGATOR analysis tool site (http://www.genevestigator.ethz.ch) [ 20].
The use of automated technology to collect continuous records of animal association data is increasing, and the development of analytical techniques to deal with these large and complex data sets are being explored.
Currently, the ABA database mainly serves as an online reference for visual inspection of individual genes; the underlying rich information of this large data set is yet to be explored by novel computational tools.
Each data set was analyzed using NONMEM 7.2 with FOCEI and with SAEM to explore algorithm performances.
Each data set was normalized separately.
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