While estimating confidence intervals for model parameters, possible clustering of survival probabilities within project sites was adjusted for by considering each site as a cluster and taking into account within-cluster correlation of observations.
In Fig. 9a, a cluster of the detection probability of type N maneuver (type P maneuver when ψ < 0) at different thresholds is exhibited.
Initially, we examined the overall patterns with tests including all samples at two cross-sections of time, contemporary and historical, here basing the groupings for the hierarchical model on the cluster of maximum membership probability for each sample (Table 1).
Overall, we were able to identify five meaningful clusters for probability of occurrence and impact: "high-impact prospects", "high-impact expectations", "high-impact eventualities", "medium-impact eventualities" and "high-impact surprises".
The results of an extensive Monte Carlo simulation study illustrate that the performance of the statistics depends on several factors including the number of clusters, cluster size, probability of success in the test procedure, the homogeneity of the probability of success across clusters, and the intra-cluster correlation coefficient (ICC).
The second feature is the product of probabilities that cluster C k is a genuine cluster given the matepairs which are assigned to C k. Larger probabilities imply that the cluster C k is reliable.
To this end, with the Bayesian theory and the cluster posterior probabilities of classes, we define a single objective function to which the clustering process is directly embedded.
Thus, assuming that its estimates were correct, a real instrument that produces a large cluster of very low pretest probabilities and a small cluster of very high pretest probabilities would begin to model the perfect results.
Fuzzy clustering permits each patient to have a probability of membership to each cluster along with the cluster number, with the sum of all cluster membership probabilities being 1 for any patient.
First we identified the districts, then we selected randomly a cluster of household with a probability of select proportional to the size of this population and all individuals aged more than 65 years were recruited into the sample.
