GCEstim (version 0.1.0)

neagging: 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
)

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 object.

coefficients

the aggregated coefficients that produced the lowest in sample error.

coefficients.object

the coefficients of the object.

Arguments

object

Fitted lmgce or tsbootgce model object.

boot.B

To use with a lmgce object. A single positive integer greater or equal to 10 for the number of bootstrap replicates for the computation of the Normalized Entropy Aggregation estimate(s), to be used when object was created with boot.B = 0. The default is boot.B = 100 when the object has no previous sampling information and boot.B = object$boot.B otherwise, which corresponds to the boot.B given to lmgce when the object was created.

boot.method

To use with a lmgce object. Method used for bootstrapping. One of c("residuals", "cases", "wild") which corresponds to resampling on residuals, on individual cases or on residuals multiplied by a N(0,1) variable, respectively. The default is boot.method = object$boot.method.

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 boot.method = object$error.

Author

Jorge Cabral,

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. tools:::Rd_expr_doi("10.1007/978-3-030-26036-1_2")

See Also

The generic functions plot.neagging and coef.neagging.

Examples

# \donttest{
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)
# }