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As the number of K was set to include four dummy clusters, the proportion of assigned salmon to Teno mainstem lower and upper populations decreased by 48%and40%0%, respectively.
S tructure analysis with 0 4 dummy clusters (K, K + 1… K + 4, see Materials and methods) revealed that the assignments to all but one tributary population remained practically unaffected despite increasing the number of K (Table 1).
To minimize the effect of individuals from nonsampled populations in the final GSI, individuals assigned to the four dummy clusters (n = 224) were filtered out, after which individual assignments were defined from K = 14 partitioning solution.
To test for significant changes in rates of GHQ caseness and frequent consultations with a general practitioner over the period on incapacity benefit, we obtained confidence intervals using a random effects logistic regression on year dummies with clustering by respondent.
Mean comparison tests (t tests) were conducted by omitting one cluster dummy variable at a time and evaluating the significance of the coefficients on the other dummy variables by examining the t-statistics associated with each estimated coefficient.
The variable of interest is then regressed on the three cluster dummy variables.
Spatial factors were dummy variables representing the cluster of sites formed at different distances and, in the full data set, distance north from the southernmost site.
Cluster analysis and dummy coding were used to prepare the data for analysis and modelling.
A cluster will have dummy nodes if it has more time slots than nodes.
Then as before we use the dummy variable method to explode cluster labels into binary features.
Each of the clusters has nodes, and there are slots in a frame, which implies that each cluster has one dummy node.
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