calc_M_hat: Function to estimate M value (M hat) for a family of T...
In okayaa/MT: Methods in Mahalanobis-Taguchi (MT) System

Description Usage Arguments Value See Also Examples

calc_M_hat estimates M values (M hat) for the T method.

1	calc_M_hat(X, beta_hat, eta_hat)

`X`	Matrix with n rows (samples) and q columns (variables). The independent variable data after the data transformation. All data should be continuous values and should not have missing values.
`beta_hat`	Vector with length q. Estimated proportionality constants between each independent variable and the dependent variable.
`eta_hat`	Vector with length q. Estimated squared signal-to-noise ratios (S/N) coresponding to `beta_hat`.

Vector with length n. Estimated M values (M hat).

general_T and general_forecasting.T

# 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 settings are same as the T1 method.
model <- general_T(unit_space_data = stackloss_center,
                   signal_space_data = stackloss_signal,
                   generates_transform_functions =
                                       generates_transformation_functions_T1,
                   includes_transformed_data = TRUE)

modified_eta_hat <- model$eta_hat
modified_eta_hat[3] <- 0

(modified_M_hat <- calc_M_hat(model$X, model$beta_hat, modified_eta_hat))