Description Usage Arguments Value Methods (by class) Examples
Estimate parameters of a linear model by matching the moments of kernel density estimators.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | mmKDE(formula, data = list(), xin, type, ...)
## Default S3 method:
mmKDE(formula, data = list(), xin, type, ...)
## S3 method for class 'mmKDE'
print(x, ...)
## S3 method for class 'mmKDE'
summary(object, ...)
## S3 method for class 'summary.mmKDE'
print(x, ...)
## S3 method for class 'formula'
mmKDE(formula, data = list(), xin, type, ...)
## S3 method for class 'mmKDE'
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 |
... |
Arguments to be passed on to the control argument of the |
x |
An mmKDE object. |
object |
An mmKDE object. |
newdata |
The data on which the estimated model is to be fitted. |
A generic S3 object with class mmKDE.
mmKDE.default: A list with all components from optim
, as well as:
intercept: Did the model contain an intercept TRUE/FALSE?
coefficients: A vector of estimated coefficients.
df: Degrees of freedom of the model.
error: The value of the objective function.
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.
summary.mmKDE: A list of class summary.mmKDE with the following components:
call: Original call to mmKDE
function.
coefficients: A vector with parameter estimates.
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.
residSum: Summary statistics for the distribution of the residuals.
print.summary.mmKDE: The object passed to the function is returned invisibly.
predict.mmKDE: A vector of predicted values resulting from the estimated model.
default
: default method for mmKDE.
mmKDE
: print method for mmKDE.
mmKDE
: summary method for mmKDE.
summary.mmKDE
: print method for summary.mmKDE.
formula
: formula method for mmKDE.
mmKDE
: predict method for mmKDE.
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