general_forecasting.T: General function to implement a forecasting method for a...
In okayaa/MT: Methods in Mahalanobis-Taguchi (MT) System

Description Usage Arguments Value See Also Examples

general_forecasting.T is the general function that implements a forecasting method for a family of Taguchi (T) methods. Each forecasting method of a family of T methods can be implemented by setting the parameters of this function appropriately.

1	general_forecasting.T(model, newdata, includes_transformed_newdata = FALSE)

`model`	Object generated as a model.
`newdata`	Matrix with n rows (samples) and p columns (variables). The data are used to calculate the desired distances from the unit space. All data should be continuous values and should not have missing values.
`includes_transformed_newdata`	If `TRUE`, then the transformed data for `newdata` are included in a return object.

A list containing the following components is returned.

`M_hat`	Vector with length n. The estimated values of the dependent variable after the data trasformation.
`y_hat`	Vector with length n. The estimated values after the inverse transformation from `M_hat`.
`model`	Object passed by `model`.
`n`	The number of samples for `newdata`.
`q`	The number of variables after the data transformation.
`X`	If `includes_transformed_newdata` is `TRUE`, then the transformed data for `newdata` are included.

forecasting.T1, forecasting.Ta, and forecasting.Tb

# The value of the dependent variable of the following samples mediates
# in the stackloss dataset.
stackloss_center <- stackloss[c(9, 10, 11, 20, 21), ]

# The following samples are data other than the unit space data and the test
# data.
stackloss_signal <- stackloss[-c(2, 9, 10, 11, 12, 19, 20, 21), ]

# The following settings are same as the T1 method.
model <- general_T(unit_space_data = stackloss_center,
                   signal_space_data = stackloss_signal,
                   generates_transform_functions =
                                       generates_transformation_functions_T1,
                   subtracts_V_e = TRUE,
                   includes_transformed_data = TRUE)

# The following test samples are chosen casually.
stackloss_test <- stackloss[c(2, 12, 19), -4]

forecasting <- general_forecasting.T(model = model,
                                     newdata = stackloss_test,
                                     includes_transformed_newdata = TRUE)

(forecasting$y_hat) # Estimated values
(stackloss[c(2, 12, 19), 4]) # True values