mmKDEboot: Moment matching for kernel density estimators.
In alR: Arc Lengths of Statistical Functions

Description Usage Arguments Value Methods (by class)

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

mmKDEboot(formula, data = list(), xin, type, bootstraps, bootName, ...)

## Default S3 method:
mmKDEboot(formula, data = list(), xin, type, bootstraps,
  bootName, ...)

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

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

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

## S3 method for class 'formula'
mmKDEboot(formula, data = list(), xin, type, bootstraps,
  bootName, ...)

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

`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`.
`bootstraps`	An integer giving the number of bootstrap samples.
`bootName`	The name of the .rds file to store the mmKDEboot object. May include a path.
`...`	Arguments to be passed on to the control argument of the `optim` function.
`x`	An mmKDEboot object.
`object`	An mmKDEboot object.
`newdata`	The data on which the estimated model is to be fitted.

A generic S3 object with class mmKDEboot.

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

intercept: Did the model contain an intercept TRUE/FALSE?
coefficients: A vector of estimated coefficients.
coefDist The bootstrap parameter distribution.
bcoefficients: A vector of bootstrap coefficients, resulting from bootstrap estimation.
df: Degrees of freedom of the model.
se: The standard errors for the estimates resulting from bootstrap estimation.
error: The value of the objective function.
errorList: A vector of values of the objective function for each bootstrap sample.
fitted.values: A vector of estimated values.
residuals: The residuals resulting from the fitted model.
call: The call to the function.
h_y: The KDE bandwidth estimator for the dependent variable.
h_X: The KDE bandwidth estimator for the independent variables, i.e. \mathbf{X}\underline{\hat{β}}.
MOMy: The first n non central moments of the dependent variable, where n is the number of columns in the design matrix.
MOMX: The first n non central moments of the independent variables \mathbf{X}\underline{\hat{β}}, where n is the number of columns in the design matrix.
time: Min, mean and max time incurred by the computation, as obtained from comm.timer.

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

call: Original call to mmKDEboot function.
coefficients: A matrix with estimates, estimated errors, and 95% parameter confidence intervals (based on the inverse empirical distribution function).
moments: A matrix of the first n moments of the dependent and independent variables that were matched. The final row corresponds to the estimated bandwidth parameters for each, i.e. h_y and h_X, respectively.
r.squared: The r^{2} coefficient.
adj.r.squared: The adjusted r^{2} coefficient.
sigma: The residual standard error.
df: Degrees of freedom for the model.
error: Value of the objective function.
time: Min, mean and max time incurred by the computation, as obtained from comm.timer.
residSum: Summary statistics for the distribution of the residuals.
errorSum: Summary statistics for the distribution of the value of the objective function.