Ta {MTSYS} | R Documentation |
Function to generate a prediction expression for the Ta method
Description
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.
Usage
Ta(sample_data, subtracts_V_e = TRUE, includes_transformed_data = FALSE)
Arguments
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 |
Value
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 signal-to-noise
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 signal-to-noise 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 |
References
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.
See Also
general_T
,
generates_transformation_functions_T1
, and
forecasting.Ta
Examples
model_Ta <- Ta(sample_data = stackloss[-c(2, 12, 19), ],
subtracts_V_e = TRUE,
includes_transformed_data = TRUE)
(model_Ta$M_hat)