generates_model: Wrapper function to generate a model for a family of Taguchi...
In okayaa/MT: Methods in Mahalanobis-Taguchi (MT) System

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.

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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`. All data should be continuous values and should not have missing values.
`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 `TRUE`, then the error variance is subtracted in the numerator when calculating `eta_hat`.
`includes_transformed_data`	If `TRUE`, then the transformed data are included in a return object.

A returned object depends on the selected method. See T1, Ta or Tb.

T1, Ta, Tb

# 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)