Returns the fitted surface values \(\hat f_j(\lambda_i)\) for every sample
and variable: the point on the surface at which sample \(i\) sits, written
back in the coordinates of the original variables. Row \(i\) is the
surface's reconstruction of sample \(i\) – what the fit says the sample
would be if it lay exactly on the surface – and the residual
\(x_i - \hat f(\lambda_i)\) is the part of the sample the surface does not
capture, which is what predictivity summarises.
Usage
# S3 method for class 'prinsurf'
fitted(object, ...)Details
Values are in the working units used for fitting: centred, and divided by
each variable's standard deviation if the surface was fitted with
scale = TRUE. This is the scale on which residuals and
predictivity are computed. predict.prinsurf
differs in two ways: it reads values off the plotted contour grid rather than
evaluating the coordinate functions exactly, and it returns them on the
variables' original scales.
See also
predictivity for the per-sample quality of this
reconstruction, and predict.prinsurf for the values a reader
obtains from the contours.