Description Usage Arguments Details Value Author(s) See Also

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.

1 2 3 4 5 |

`object` |
an object of class |

`newdata` |
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 |

`type` |
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 ```
type =
"curve"
```

, the fitted locations on the principal curve. For ```
type
= "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
attributed `tag`

.

Gavin L. Simpson

See `prcurve`

for details on fitting principal curves and
an example.

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