Description Usage Arguments Value References See Also Examples
Ta
generates a prediction expression for the Ta method. In
general_T
, the data are normalized by subtracting the mean
and without scaling based on sample_data
. The sample data are not
divided into 2 datasets. All the sample data are used for both unit space
and signal space.
1 
sample_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. 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 
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.Ta
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