MTSYS (version 1.2.0)

forecasting.Ta: Forecasting method for the Ta method

Description

forecasting.Ta (via forecasting) estimates the dependent values based on the Ta model.

Usage

# S3 method for Ta
forecasting(model, newdata, includes_transformed_newdata = FALSE)

Arguments

model

Object of class "Ta" generated by Ta or generates_model(..., method = "Ta").

newdata

Matrix with n rows (samples) and p columns (variables). The Data to be estimated. All data should be continuous values and should not have missing values.

includes_transformed_newdata

If TRUE, then the transformed data for newdata are included in a return object.

Value

A list containing the following components is returned.

M_hat

Vector with length n. The estimated values of the dependent variable after the data transformation.

y_hat

Vector with length n. The estimated values after the inverse transformation from M_hat.

model

Object of class "Ta" passed by model.

n

The number of samples for newdata.

q

The number of variables after the data transformation. q equals p.

X

If includes_transformed_newdata is TRUE, then the transformed data for newdata are included.

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_forecasting.T and Ta

Examples

# NOT RUN {
model_Ta <- Ta(sample_data = stackloss[-c(2, 12, 19), ],
               subtracts_V_e = TRUE,
               includes_transformed_data = TRUE)

forecasting_Ta <- forecasting(model = model_Ta,
                              newdata = stackloss[c(2, 12, 19), -4],
                              includes_transformed_newdata = TRUE)

(forecasting_Ta$y_hat) # Estimated values
(stackloss[c(2, 12, 19), 4]) # True values

# }