neagging {GCEstim} | R Documentation |
Normalized Entropy Aggregation for Inhomogeneous Large-Scale Data - Neagging
Description
Computes the estimates for the Normalized Entropy Aggregation
Usage
neagging(
object,
boot.B = ifelse(object$boot.B == 0, 100, object$boot.B),
boot.method = object$boot.method,
error = object$error
)
Arguments
object |
|
boot.B |
To use with a |
boot.method |
To use with a |
error |
Loss function (error) to be used for the selection
of the support spaces. One of c("RMSE","MSE", "MAE", "MAPE", "sMAPE", "MASE").
The default is |
Value
An object of class
neagging
is a list containing
at least the following components:
matrix |
a matrix where each column contains sequentially the aggregated estimates. |
error |
a named vector with the in sample error for each aggregated set of estimates. |
error.object |
the in sample error of the |
coefficients |
the aggregated coefficients that produced the lowest in sample error. |
coefficients.object |
the coefficients of the |
Author(s)
Jorge Cabral, jorgecabral@ua.pt
References
da Conceição Costa, M. and Macedo, P. (2019). Normalized Entropy Aggregation for Inhomogeneous Large-Scale Data. In O. Valenzuela, F. Rojas, H. Pomares, & I. Rojas (Eds.), Theory and Applications of Time Series Analysis (pp. 19–29). Springer International Publishing. doi:10.1007/978-3-030-26036-1_2
See Also
The generic functions plot.neagging
and
coef.neagging
.
Examples
res_gce_package <-
lmgce(y ~ .,
data = dataGCE,
boot.B = 50,
seed = 230676)
neagging(res_gce_package, boot.method = "cases")
res.tsbootgce <-
tsbootgce(
formula = CO2 ~ 1 + L(GDP, 1) + L(EPC, 1) + L(EU, 1),
data = moz_ts)
neagging(res.tsbootgce)