# 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

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

## Usage

 `1` ```calc_M_hat(X, beta_hat, eta_hat) ```

## Arguments

 `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`.

## Value

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

`general_T` and `general_forecasting.T`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19``` ```# 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 <- 0 (modified_M_hat <- calc_M_hat(model\$X, model\$beta_hat, modified_eta_hat)) ```