forecasting.T1: Forecasting method for the T1 method
In okayaa/MT: Methods in Mahalanobis-Taguchi (MT) System

Description Usage Arguments Value References See Also Examples

forecasting.T1 (via forecasting) estimates the dependent values based on the T1 model.

1 2	## S3 method for class 'T1' forecasting(model, newdata, includes_transformed_newdata = FALSE)

`model`	Object of class "T1" generated by `T1` or `generates_model`(..., method = "T1").
`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 "T1" 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.

Taguchi, G. (2006). Objective Function and Generic Function (12). Journal of Quality Engineering Society, 14(3), 5-9. (In Japanese)

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 T1

# 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), ]

model_T1 <- T1(unit_space_data = stackloss_center,
               signal_space_data = stackloss_signal,
               subtracts_V_e = TRUE,
               includes_transformed_data = TRUE)

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

forecasting_T1 <- forecasting(model = model_T1,
                              newdata = stackloss_test,
                              includes_transformed_newdata = TRUE)

(forecasting_T1$y_hat) # Estimated values
(stackloss[c(2, 12, 19), 4]) # True values