Description Usage Arguments Details Value Note See Also Examples
Calculate estimators and one and twosided CIs based on local polynomial estimator under secondorder Taylor or Hölder smoothness class.
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formula 
object of class 
data 
optional data frame, list or environment (or object coercible by

subset 
optional vector specifying a subset of observations to be used in the fitting process. 
point 
specifies the point X_0 at which to do inference 
M 
Bound on second derivative of the conditional mean function. 
kern 
specifies kernel function used in the local regression. It can
either be a string equal to 
na.action 
function which indicates what should happen when the data
contain 
opt.criterion 
Optimality criterion that bandwidth is designed to optimize. It can either be based on exact finitesample maximum bias and finitesample estimate of variance, or asymptotic approximations to the bias and variance. The options are:
The finitesample methods use conditional variance given by

h 
Bandwidth. If not supplied, optimal bandwidth is computed according
to criterion given by 
se.method 
Vector with methods for estimating standard error of
estimate. If

alpha 
determines confidence level, 
beta 
Determines quantile of excess length to optimize, if bandwidth optimizes given quantile of excess length of onesided confidence intervals. 
J 
Number of nearest neighbors, if "nn" is specified in

sclass 
Smoothness class, either 
order 
Order of local regression 1 for linear, 2 for quadratic. 
se.initial 
Method for estimating initial variance for computing optimal bandwidth. Ignored if data already contains estimate of variance.

The bandwidth is calculated to be optimal for a given performance criterion,
as specified by opt.criterion
. It is calculated using the function
LPPOptBW
. Alternatively, the bandwidth can be specified by
h
.
Returns an object of class "LPPResults"
. The function
print
can be used to obtain and print a summary of the results. An
object of class "LPPResults"
is a list containing the following
components
estimate
Point estimate. This estimate is MSEoptimal if
opt.criterion="MSE"
maxbias
Maximum bias of estimate
sd
Standard deviation of estimate
lower
, upper
Lower (upper) endpoint of a onesided CI
based on estimate
. This CI is optimal if
opt.criterion="OCI"
hl
Halflength of a twosided CI based on estimate
, so
that the CI is given by c(estimatehl, estimate+hl)
. The
CI is optimal if opt.criterion="FLCI"
eff.obs
Effective number of observations used by
estimate
h
Bandwidth used
naive
Coverage of CI that ignores bias and uses
qnorm(1alpha/2)
as critical value
call
the matched call
subset
is evaluated in the same way as variables in formula
,
that is first in data
and then in the environment of formula
.
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