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 |
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$eta_hat
modified_eta_hat[3] <- 0
(modified_M_hat <- calc_M_hat(model$X, model$beta_hat, modified_eta_hat))
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