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Here also, we grouped the data collected from all systems studied to have a single set of data.
The first two components (Figure 5, PCA1 and PCA2) accounted for 59.2% of the variance in the data, and grouped the data into three clusters.
Accordingly we grouped the data into two groups: group one contains data for the fair-weather days from January 3 to January 15 and group two contains data for the days January 25, February 1 and 5.
For each forest type (or plant functional type, as we grouped the data in this analysis), data describing its NEP response as a function of time since harvest was also needed.
To examine their seasonal variations, we grouped the data into the following four seasonal bins: summer (May, June, July and August), vernal equinox (March and April), autumn equinox (September and October), and winter (January, February, November and December) (Maruyama and Matuura 1984; Maruyama et al. 2009; Sripathi et al. 2011; Xiao and Zhang 2001).
We grouped the data into broad categories of ecosystem types.
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I also consider the robustness of results to a slightly different way of grouping the data.
Using cluster analysis as a preliminary task can group the data into different homogeneous segments.
The aim of clustering is to group the data objects into multiple clusters in terms of similarity.
Standard AMOVAs were used to test for the significance of grouping the data by island, species, and taxonomic variety.
For binary data, the issue of grouping the data by covariates has been discussed.
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