Arguments
x
Independent variable (or matrix).
weights
Prior weights for observations (reciprocal of variance, or sample size).
cens
Censoring indicators for hazard rate or censored regression.
base
Baseline parameter estimate.
xlim
For density estimation, optional vector of lower and upper
bounds for variables.
flim
A vector of lower and upper bounds for the evaluation structure.
Defaults to the data range.
scale
A scale to apply to each variable. Effectively, the data is
transformed before fitting, by dividing each component of the
independent variable by the corresponding component of scale.
alpha
Smoothing parameter. A single number (e.g. alpha=0.7)
is interpreted as a nearest neighbor fraction. With two
componentes (e.g. alpha=c(0.7,1.2)), the first component
is a nearest neighbor fraction, and the second component is
a
ev
Evaluation Structure, default = "tree". Also available are
"phull", "data", "grid", "kdtree", "kdcenter", "crossval".
deg
Degree of local polynomial. Default: 2 (local quadratic).
family
Local likelihood family; "gaussian";
"binomial"; "poisson"; "gamma" and "geom".
Density and rate estimation families are "dens", "rate" and
"hazard"
link
Link function for local likelihood fitting. Depending on the family,
choices may be "ident", "log", "logit", "inverse",
"sqrt".
maxk
Controls space assignment for evaluation structures, default 50
kern
Weight function, default = "tcub". Others are "rect", "trwt",
"tria", "epan", "bisq" and "gauss". Choices may be restricted
when derivatives are required; e.g. for
kt
Kernel type, "sph" (default); "prod".
In multivariate problems, \Co{"prod"} uses a
simplified product model which speeds up computations.
itype
Integration type for density estimation.
mint
Points for numerical integration rules. Default 20.
maxit
Maximum iterations for local likelihood estimation. Default 20.
cut
Refinement parameter for adaptive partitions. Default 0.8; smaller
values result in more refined partitions.