Description Usage Arguments Value Methods (by class)
Bias corrected jackknife estimates, along with standard errors and confidence intervals, of a linear model, resulting from moment matching of kernel density estimates.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | 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, ...)
|
formula |
An LHS ~ RHS formula, specifying the linear model to be estimated. |
data |
A data.frame which contains the variables in |
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 |
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 |
x |
An mmKDEjack object. |
object |
An mmKDEjack object. |
newdata |
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{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.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.
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