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 |

`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 |

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 |

`model` |
Object of class "T1" passed by |

`n` |
The number of samples for |

`q` |
The number of variables after the data transformation. q equals p. |

`X` |
If |

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.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | ```
# 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
``` |

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