forecasting.Tb: Forecasting method for the Tb method
In okayaa/MT: Methods in Mahalanobis-Taguchi (MT) System

Description Usage Arguments Value References See Also Examples

forecasting.Tb (via forecasting) estimates the dependent values based on the Tb model.

1 2	## S3 method for class 'Tb' forecasting(model, newdata, includes_transformed_newdata = FALSE)

`model`	Object of class "Tb" generated by `Tb` or `generates_model`(..., method = "Tb").
`newdata`	Matrix with n rows (samples) and p columns (variables). The Data to be estimated. 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 transformation.
`y_hat`	Vector with length n. The estimated values after the inverse transformation from `M_hat`.
`model`	Object of class "Tb" passed by `model`.
`n`	The number of samples for `newdata`.
`q`	The number of variables after the data transformation. q equals p.
`X`	If `includes_transformed_newdata` is `TRUE`, then the transformed data for `newdata` are included.

Inou, A., Nagata, Y., Horita, K., & Mori, A. (2012). Prediciton Accuracies of Improved Taguchi's T Methods Compared to those of Multiple Regresssion Analysis. Journal of the Japanese Society for Quality Control, 42(2), 103-115. (In Japanese)

Kawada, H., & Nagata, Y. (2015). An application of a generalized inverse regression estimator to Taguchi's T-Method. Total Quality Science, 1(1), 12-21.

general_forecasting.T and Tb

model_Tb <- Tb(sample_data = stackloss[-c(2, 12, 19), ],
               subtracts_V_e = TRUE,
               includes_transformed_data = TRUE)

forecasting_Tb <- forecasting(model = model_Tb,
                              newdata = stackloss[c(2, 12, 19), -4],
                              includes_transformed_newdata = TRUE)

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