a data frame expanded from a rectangular marginal grid points (points of
evaluations) in the space of the predictors (see
expand.grid()).
weights
a numeric vector of weights to be given to individual observations
in the sum of squared residuals that forms the local fitting criterion.
span
smoothing parameter.
degree
overall degree of locally-fitted polynomial.
1 is locally-linear fitting and
2 is locally-quadratic fitting.
parametric
for two or more numeric predictors, this argument
specifies those variables that should
be conditionally-parametric. It should be specified as
a logical vector of length equal to the number of columns in
x.evaluate.
drop.square
for cases with
degree equal to
2 and with two or more numeric predictors,
this argument
specifies those numeric predictors whose squares should be dropped from
the set of fitting
variables.
The method of specification is the same as
for
parametric.
cell
the maximum cell size of the k-d tree.
Suppose k <- floor(n*cell*span) where
n is the number of observations.
Then a cell is further divided if the number of observations within it
is greater than or equal to
k.
s
standard deviation.
VALUE:
This is a support routine for
predict.loess(). It returns the standard
errors of the fitted values.