kappa0 {locfit} | R Documentation |
The geometric constants for simultaneous confidence bands are computed,
as described in Sun and Loader (1994) (bias adjustment is not implemented
here). These are then passed to the crit
function, which
computes the critical value for the confidence bands.
The method requires both the weight diagrams l(x), the
derivative l'(x) and second derivatives l''(x). These are
implemented exactly for a constant bandwidth; that
is, alpha=c(0,h)
for some h
. For nearest
neighbor bandwidths, the computations are approximate.
The theoretical justification for the bands are computed using the spherical symmetry of the Normal distributions. For non-normal distributions, and likelihood models, one relies on central limit and related theorems...
Computation uses the product Simpson's rule to evaluate the
multidimensional integrals. Expect this to be slow in more
than one dimension. The mint
argument controls the
precision.
kappa0(formula, cov=0.95, ...)
formula |
Local regression model formula. |
cov |
Coverage Probability for critical values. |
ldots |
Other arguments to locfit .
|
A list with components for the critical value, geometric constants,
e.t.c. Can be passed directly to plot.locfit
as the
crit
argument.
Sun, J. and Loader, C. (1994). Simultaneous confidence bands for linear regression and smoothing. Annals of Statistics 22, 1328-1345.
locfit
, plot.locfit
,
crit
, crit<-
.
# compute and plot simultaneous confidence bands data(ethanol) fit <- locfit(NOx~E,data=ethanol) crit(fit) <- kappa0(NOx~E,data=ethanol) plot(fit,crit=crit,band="local")