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A two-dimensional analysis (Figure 5D) showed that the error function was sensitive to the values of the two parameters independently and that the values determined by the fitting process represented a true minimum over this parameter space.
It is also possible to check the validity of the algorithm in real data by exploring to what extent the corrections made by the fitting process are plausible.
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Stability of the fits was analyzed by repeating the fitting process with each of various cell types omitted (i.e. "leave one out" testing); we observed that there was only a very small disturbance in the results of the fit by the remaining cells.
We achieve this goal by augmenting the fitting process of standard AAMs by including various types of constraints.
A hand-held computer is used by a clinician during the fitting process to set the stimulation parameters (eg, intensity, pulse frequency) and timing of the stimulation.
To take into consideration the different deviations of the core-genome size medians, as clearly indicated by the blue error bars in Figure 3a, we modified the fitting process by introducing the genome number as weight to the corresponding data point.
However, in contrast to standard AAMs, we augment the fitting process by imposing constraints obtained from sources such as subset methods [16, 30].
The fitting algorithm increments the connectivity during the fitting process until a satisfactory fit is obtained.
The fitting process is guided by the curvature-based feature map characteristic of the faces.
The "fitting process" is guided by the surgical plan, generated in computer simulations of this phase of the surgery.
The quality of the fitting process is measured by the value -squared : a number from to, which is the normalized square of the residuals of the data after the fit.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com