Description Usage Arguments Value See Also Examples

`generates_model`

generates a model for a family of Taguchi (MT) methods.
The model of `T1`

method, `Ta`

method or the
`Tb`

method can be generated by passing a method name
(character) into a parameter `method`

.

1 2 3 | ```
generates_model(unit_space_data, signal_space_data, sample_data,
method = c("T1", "Ta", "Tb"), subtracts_V_e = TRUE,
includes_transformed_data = FALSE)
``` |

`unit_space_data` |
Used only for the T1 method. Matrix with n rows
(samples) and (p + 1) columns (variables). The 1 ~ p
th columns are independent variables and the (p + 1)
th column is a dependent variable. Underlying data
to obtain a representative point for the
normalization of |

`signal_space_data` |
Used only for the T1 method. Matrix with m rows (samples) and (p + 1) columns (variables). The 1 ~ p th columns are independent variables and the (p + 1) th column is a dependent variable. Underlying data to generate a prediction expression. All data should be continuous values and should not have missing values. |

`sample_data` |
Used for the Ta and the Tb methods. Matrix with n rows (samples) and (p + 1) columns (variables). The 1 ~ p th columns are independent variables and the (p + 1) th column is a dependent variable. All data should be continuous values and should not have missing values. |

`method` |
Character to designate a method. Currently, "MT", "MTA", and "RT" are available. |

`subtracts_V_e` |
If |

`includes_transformed_data` |
If |

A returned object depends on the selected method. See `T1`

,
`Ta`

or `Tb`

.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 | ```
# 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 test samples are chosen casually.
stackloss_test <- stackloss[c(2, 12, 19), -4]
# T1 method
model_T1 <- generates_model(unit_space_data = stackloss_center,
signal_space_data = stackloss_signal,
method = "T1",
subtracts_V_e = TRUE)
forecasting_T1 <- forecasting(model = model_T1,
newdata = stackloss_test)
(forecasting_T1$y_hat)
# Ta method
model_Ta <- generates_model(sample_data =
rbind(stackloss_center, stackloss_signal),
method = "Ta",
subtracts_V_e = TRUE)
forecasting_Ta <- forecasting(model = model_Ta,
newdata = stackloss_test)
(forecasting_Ta$y_hat)
# Tb method
model_Tb <- generates_model(sample_data =
rbind(stackloss_center, stackloss_signal),
method = "Tb",
subtracts_V_e = TRUE)
forecasting_Tb <- forecasting(model = model_Tb,
newdata = stackloss_test)
(forecasting_Tb$y_hat)
``` |

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