predict.locfit {locfit}  R Documentation 
The locfit
function computes a local fit at a selected set
of points (as defined by the ev
argument). The predict.locfit
function is used to interpolate from these points to any other points.
The method is based on cubic hermite polynomial interpolation, using the
estimates and local slopes at each fit point.
The motivation for this twostep procedure is computational speed.
Depending on the sample size, dimension and fitting procedure, the
local fitting method can be expensive, and it is desirable to keep the
number of points at which the direct fit is computed to a minimum.
The interpolation method used by predict.locfit()
is usually
much faster, and can be computed at larger numbers of points.
## S3 method for class 'locfit': predict(object, newdata, where="fitp", se.fit=FALSE, band="none", what="coef", ...)
object 
Fitted object from locfit() .

newdata 
Points to predict at. Can be given in several forms: vector/matrix; list, data frame. 
where 
An alternative to texttt{newdata}. Choices include "grid"
for the grid lfmarg(object) ; "data" for the original
data points and "fitp" for the direct fitting points (ie. no
interpolation).

se.fit 
If TRUE , standard errors are computed along with the fitted values.

band 
Compute standard errors for the fit and include confidence bands on
the returned object. Default is "none" . Other
choices include "global" for bands using a global variance estimate;
"local" for bands using a local variance estimate and "pred"
for prediction bands (at present, using a global variance estimate).
To obtain the global variance estimate for a fit, use rv .
This can be changed with rv< . Confidence bands, by default,
are 95
To change the critical value or confidence level, or to obtain
simultaneous instead of pointwise confidence,
the critical value stored on the fit must be changed. See the
kappa0 and crit functions.

what 
What to compute predicted values of. The default, what="coef" , works
with the fitted curve itself. Other choices include "nlx" for the
length of the weight diagram; "infl" for the influence function;
"band" for the bandwidth; "degr" for the local polynomial
degree; "lik" for the maximized local likelihood; "rdf"
for the local residual degrees of freedom and "vari" for the
variance function. The interpolation algorithm for some of these quantities
is questionable.

... 
Additional arguments to preplot.locfit .

If se.fit=F
, a numeric vector of predictors.
If se.fit=T
, a list with components fit
, se.fit
and
residual.scale
.
data(ethanol) fit < locfit(NOx~E,data=ethanol) predict(fit,c(0.6,0.8,1.0))