Bias corrected jackknife estimates, along with standard errors and confidence intervals, of a linear model, resulting from arc length matching of kernel density estimates.
alKDEjack(formula, data = list(), xin, q1, q2, type, jackName, ...)# S3 method for default
alKDEjack(formula, data = list(), xin, q1, q2, type,
jackName, ...)
# S3 method for alKDEjack
print(x, ...)
# S3 method for alKDEjack
summary(object, ...)
# S3 method for summary.alKDEjack
print(x, ...)
# S3 method for formula
alKDEjack(formula, data = list(), xin, q1, q2, type,
jackName, ...)
# S3 method for alKDEjack
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.
Numeric vectors, for the lower and upper bounds of the intervals over which arc lengths are to be computed.
An integer specifying the bandwidth selection method used, see bw.
The name of the .rds file to store the alKDEjack object. May include a path.
Arguments to be passed on to the control argument of the optim function.
An alKDEjack object.
An alKDEjack object.
The data on which the estimated model is to be fitted.
A generic S3 object with class alKDEjack.
alKDEjack.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 jackknife coefficients, resulting from jackknife estimation.
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. \(\mathbf{X}\underline{\hat{\beta}}\).
ALy: Arc length segments of the KDE cast over the dependent variable.
ALX: Arc length segments of the KDE cast over the independent variables \(\mathbf{X}\underline{\hat{\beta}}\).
time: Min, mean and max time incurred by the computation, as obtained from comm.timer.
p1: The vector of quantiles in the domain of \(y\) corresponding to q1.
p2: The vector of quantiles in the domain of \(y\) corresponding to q2.
summary.alKDEjack: A list of class summary.alKDEjack with the following components:
call: Original call to the alKDEjack function.
coefficients: A matrix with estimates, estimated errors, and 95% parameter confidence intervals (based on the inverse empirical distribution function).
arclengths: A matrix of the arc length segments that were matched, for the dependent and independent variables. 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.alKDEjack: The object passed to the function is returned invisibly.
predict.alKDEjack: A vector of predicted values resulting from the estimated model.
default: default method for alKDEjack.
alKDEjack: print method for alKDEjack.
alKDEjack: summary method for alKDEjack.
summary.alKDEjack: print method for summary.alKDEjack.
formula: formula method for alKDEjack.
alKDEjack: predict method for alKDEjack.