mmKDEjack: Moment matching for kernel density estimators.

Description Usage Arguments Details Value Methods (by class)

Description

Bias corrected jackknife estimates, along with standard errors and confidence intervals, of a linear model, resulting from moment matching of kernel density estimates.

Usage

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mmKDEjack(formula, data = list(), xin, type, jackName, ...)

## Default S3 method:
mmKDEjack(formula, data = list(), xin, type, jackName, ...)

## S3 method for class 'mmKDEjack'
print(x, ...)

## S3 method for class 'mmKDEjack'
summary(object, ...)

## S3 method for class 'summary.mmKDEjack'
print(x, ...)

## S3 method for class 'formula'
mmKDEjack(formula, data = list(), xin, type, jackName, ...)

## S3 method for class 'mmKDEjack'
predict(object, newdata = NULL, ...)

Arguments

formula

An LHS ~ RHS formula, specifying the linear model to be estimated.

data

A data.frame which contains the variables in formula.

xin

Numeric vector of length equal to the number of independent variables, of initial values, for the parameters to be estimated.

type

An integer specifying the bandwidth selection method used, see bw.

jackName

The name of the .rds file to store the mmKDEjack object. May include a path.

...

Arguments to be passed on to the control argument of the optim function.

x

An mmKDEjack object.

object

An mmKDEjack object.

newdata

The data on which the estimated model is to be fitted.

Details

On systems where the pbMPI package is available, this code will run in parallel.

Value

A generic S3 object with class mmKDEjack.

mmKDEjack.default: A list object (saved using saveRDS in the specified location) with the following components:

summary.mmKDEjack: A list of class summary.mmKDEjack with the following components:

print.summary.mmKDEjack: The object passed to the function is returned invisibly.

predict.mmKDEjack: A vector of predicted values resulting from the estimated model.

Methods (by class)


mtloots/alR documentation built on May 23, 2019, 8:18 a.m.