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Over the last two decades, researchers have developed a class of statistical models designed for data with hierarchical structure.
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In the ordinary regression model, these independent variables are all on the same level, which results in the loss of the information contained in data with hierarchical structures.
Gene diversity statistics for microsatellite variation among tetraploid populations was not calculated, but respective data are analyzed and displayed with hierarchical Structure analysis (see Figure 5).
Firstly, we establish a SDON architecture with hierarchical structure of data plane, which divides the data plane into four layers for getting fine-grained bandwidth resource.
In this paper, we examine in detail several methods that correct for confounding on discrete data with hierarchical population structure.
We examine several methods that correct for confounding on discrete data with hierarchical population structure and identify two distinct confounding processes, which we call coevolution and conditional influence.
The field data had a hierarchical structure with plots within clusters.
For all responses, the data had a hierarchical structure with varying numbers (12 71) of fish sampled from the 30 sites.
Usually, such data have a hierarchical structure, with patients clustered within physicians, and physicians clustered within practices.
The data had a hierarchical structure with individual patients at level one, and health authorities (or hospitals) at level two.
The data had a hierarchical structure, with the patient as the first level and the HD as the second level.
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