loess.ise(y, x, x.evaluate, weights, span, degree, parametric,
      drop.square, cell, s)

y:

a numeric vector of response

x:

a matrix of numeric predictors

x.evaluate:

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.