Description Usage Arguments Value References See Also Examples

`forecasting.Ta`

(via `forecasting`

) estimates the dependent
values based on the Ta model.

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

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

`n` |
The number of samples for |

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

`X` |
If |

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 | ```
model_Ta <- Ta(sample_data = stackloss[-c(2, 12, 19), ],
subtracts_V_e = TRUE,
includes_transformed_data = TRUE)
forecasting_Ta <- forecasting(model = model_Ta,
newdata = stackloss[c(2, 12, 19), -4],
includes_transformed_newdata = TRUE)
(forecasting_Ta$y_hat) # Estimated values
(stackloss[c(2, 12, 19), 4]) # True values
``` |

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