Making cool pictures has a purpose only if both the pics and the numbers
behind them are accurate. It's not an aim in itself, I hope (although
this is the second time I hear that one should add as many points as
needed to see a nice picture). Parsimonious explanations are, to me,
much more
Folks,
I think it is important to recognize that the example in Andrea’s earlier post
does not really address the validity of sliding semilandmark methods, because
all of the data were simulated using isotropic error. Thus, the points called
semilandmarks in that example were actually
Igor,
The components, $means contain the least squares means from the linear model
implemented in trajectory analysis. These can be visualized relative to some
reference (e.g., the overall mean shape), using ‘plotRefToTarget’. Note that
the $means must first be converted to a 2D array using
