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We present a method to detect heavily clustered variables and to identify physicians with outlying measurements.
Additionally, a limited survey was conducted among physicians with unusually high or low random intercepts for highly clustered variables.
Peritumoral tissues display a more homogeneous distribution of clustered variables, while as expected, tumor samples exhibit a higher levels of heterogeneity.
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Most articles reviewed focused on sleep as a concurrently occurring or clustered variable with either fatigue or one or more other symptoms (59%).
This technique clusters variables into different groups.
Using the above four indicators as clustering variables, we conducted the cluster analysis to examine the current regional disparities of preschool education development in 31 provinces.
Furthermore, unlike principal component analysis, which clusters variables together, MCA clusters the categories within these variables together.
Furthermore, unlike PCA, which clusters variables together, MCA clusters the categories within these variables together [ 25].
In the K-means analysis on N = 4,467, all clustering variables significantly contributed to the solution (P < 0.001).
The Principal Component Analysis (PCA) is a method in clustering variables and explaining the variances of a measure [ 20].
Random effect model with admitting hospital as clustering variable.
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