Bias corrected jackknife estimates, along with standard errors and confidence intervals, of a linear model, resulting from moment matching of kernel density estimates.
mmKDEjack(formula, data = list(), xin, type, jackName, ...)# S3 method for default
mmKDEjack(formula, data = list(), xin, type, jackName, ...)
# S3 method for mmKDEjack
print(x, ...)
# S3 method for mmKDEjack
summary(object, ...)
# S3 method for summary.mmKDEjack
print(x, ...)
# S3 method for formula
mmKDEjack(formula, data = list(), xin, type, jackName, ...)
# S3 method for mmKDEjack
predict(object, newdata = NULL, ...)
An LHS ~ RHS formula, specifying the linear model to be estimated.
A data.frame which contains the variables in formula.
Numeric vector of length equal to the number of independent variables, of initial values, for the parameters to be estimated.
An integer specifying the bandwidth selection method used, see bw.
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.
An mmKDEjack object.
An mmKDEjack object.
The data on which the estimated model is to be fitted.
A generic S3 object with class mmKDEjack.
mmKDEjack.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 jackknife parameter distribution.
jcoefficients: A vector of bias-corrected coefficients, resulting from jackknife estimation.
bias: The corrections applied in obtaining the bias-corrected estimates.
df: Degrees of freedom of the model.
se: The standard errors for the estimates resulting from jackknife estimation.
error: The value of the objective function.
errorList: A vector of values of the objective function at jackknife points.
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. \(\mathbf{X}\underline{\hat{\beta}}\).
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 \(\mathbf{X}\underline{\hat{\beta}}\), 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.mmKDEjack: A list of class summary.mmKDEjack with the following components:
call: Original call to the mmKDEjack 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.
print.summary.mmKDEjack: The object passed to the function is returned invisibly.
predict.mmKDEjack: A vector of predicted values resulting from the estimated model.
default: default method for mmKDEjack.
mmKDEjack: print method for mmKDEjack.
mmKDEjack: summary method for mmKDEjack.
summary.mmKDEjack: print method for summary.mmKDEjack.
formula: formula method for mmKDEjack.
mmKDEjack: predict method for mmKDEjack.