Locations on a fitted principal curve are predicted by projecting the new observations in m dimensions on to the corresponding closest point on the curve. Fitted values for data used to fit the curve are available with respect to the principal curve or to the individual smooth functions.
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an object of class
a matrix or data frame of new observations within the space of the
orginal data. Variables are matched against those of the original
data via their
character; the type of fitted values to return.
other arguments passed to other methods. Not currently used.
Fitting a principal curve involves two procedures. In one, the current curve is bent towards the data via the fitting of spline functions with distance along the curve as the predictor variable and each variable in turn as the response. The second procedure, a projection step, involves projecting the observed points in m dimensions on to locations along the current curve to which they are closest in the hyperspace.
Given a fitted curve, the projection step can be used to find new points on the fitted curve by projecting the new points located in the hyperspace on to points on the curve to which they are closest.
Fitted values are available for the data used to the fit the principal
curve. There are two types of fitted value available. For
"curve", the fitted locations on the principal curve. For
= "smooths", the fitted values of the variables from the individual
smooth functions with respect to distance along the principal curve.
A matrix of points in the space of the original data. Rows correspond to the new samples and columns to the variables (ordered as per the original data used to fit the curve).
How these points are ordered along the fitted curve is contained in
Gavin L. Simpson
prcurve for details on fitting principal curves and
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