Description Usage Arguments Value References See Also Examples
T1
generates a prediction expression for the twosided Taguchi (T1)
method. In general_T
, the data are normalized by subtracting
the mean and without scaling based on unit_space_data
. The sample
data should be divided into 2 datasets in advance. One is for the unit
space and the other is for the signal space.
1 2 
unit_space_data 
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 the

signal_space_data 
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. 
subtracts_V_e 
If 
includes_transformed_data 
If 
A list containing the following components is returned.
beta_hat 
Vector with length q. Estimated proportionality constants between each independent variable and the dependent variable. 
subtracts_V_e 
Logical. If 
eta_hat 
Vector with length q. Estimated squared signaltonoise
ratios (S/N) coresponding to 
M_hat 
Vector with length n. The estimated values of the dependent
variable after the data transformation for 
overall_prediction_eta 
Numeric. The overall squared signaltonoise ratio (S/N). 
transforms_independent_data 
Data transformation function generated
from 
transforms_dependent_data 
Data transformation function generated from

inverses_dependent_data 
Data transformation function generated
from 
m 
The number of samples for 
q 
The number of independent variables after the data transformation. q equals p. 
X 
If 
M 
If 
Taguchi, G. (2006). Objective Function and Generic Function (12). Journal of Quality Engineering Society, 14(3), 59. (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), 103115. (In Japanese)
Kawada, H., & Nagata, Y. (2015). An application of a generalized inverse regression estimator to Taguchi's TMethod. Total Quality Science, 1(1), 1221.
general_T
,
generates_transformation_functions_T1
, and
forecasting.T1
1 2 3 4 5 6 7 8 9 10 11 12 13 14  # 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)
(model_T1$M_hat)

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