To provide a measure of uncertainty of these adherence metrics, it is useful to report the summary measures using different assumptions (e.g. excluding unknown intervals and considering these as low adherence).
Using a carefully generated synthetic data set, where we know the underlying partitioning and the distribution of the data, we apply the internal validation measures using different parameters of the clustering algorithms.
As each choice of the reference vector (s, w) might generate a different measure, we calculate two measures using different reference vectors (0, w 0) and (1, w 1) which, in fact, are observed in each period and thus are equally reasonable.
Since each choice of the reference vector (r, w) might generate a different measure, we calculate two measures using different reference vectors (r 0, w 0) and (r 1, w 1) which, in fact, are observed in each period and thus are equally reasonable.
Since each choice of the reference vector (t, r) might generate a different measure, we calculate two measures using different reference vectors (0, r 0) and (1, r 1) which, in fact, are observed in each period and thus are equally reasonable.
Figure 4 shows the results of the proposed RS measures using different SwissRegulon posterior probability cutoffs.
Although the NICOM CO and TFC measurements are already validated, CO and TFC measures use different analyses.
the two measures use different mathematical formulae to calculate the average rise in the prices of the goods and services being measured.
Different literacy measures use different cut-off points to assign participants to a category.
First, the included studies had different study designs, outcome measures, used different technologies (interventions), and explored the use of web-based technologies in different physiotherapy courses and populations.
Since different similarity measures use different types of information in GO, or model data in different ways, one measure may perform the best on certain functional categories but not on the others.
