Analyze a dataset using this model using ordinary training with the 'train' method.

Perform backward elimination to find the least predictive variable to remove from the existing model, returning the variable to eliminate, the new parameter vector and the new Quality of Fit (QoF). May be called repeatedly.

Perform backward elimination to find the least predictive variables to remove from the full model, returning the variables left and the new Quality of Fit (QoF) measures for all steps.

Build a sub-model that is restricted to the given columns of the data matrix.

Return the correlation matrix for the columns in data matrix 'xx'.

Diagnose the health of the model by computing the Quality of Fit (QoF) measures, from the error/residual vector and the predicted & actual responses. For some models the instances may be weighted.

Compute the error (difference between actual and forecasted) and useful diagnostics for the test dataset.

Compute the error (difference between actual and predicted) and useful diagnostics for the test dataset. Overridden for efficiency.

Expand the vector 'z' into a vector of that includes additional terms, i.e., add quadratic terms.

Format a double value.

Return the Quality of Fit (QoF) measures corresponding to the labels given above in the 'fitLabel' method. Note, if 'sse > sst', the model introduces errors and the 'rSq' may be negative, otherwise, R^2 ('rSq') ranges from 0 (weak) to 1 (strong). Override to add more quality of fit measures.

Return the labels for the Quality of Fit (QoF) measures. Override to add additional QoF measures.

Build a map of quality of fit measures (use of LinkedHashMap makes it ordered).

Produce a forecast for 'h' steps ahead into the future. Note: the forecasts for time 't = 0, ... , h-1' will be duplicates.

Forecast values for all time points using 1 through 'h'-steps ahead forecasts. Return a vector of forecasts for all time points.

Perform forward selection to find the most predictive variable to add the existing model, returning the variable to add and the new model. May be called repeatedly.

Perform forward selection to find the most predictive variables to have in the model, returning the variables added and the new Quality of Fit (QoF) measures for all steps.

Return the 'used' data matrix 'x'. Mainly for derived classes where 'x' is expanded from the given columns in 'x_', e.g., QuadRegression add squared columns.

Return the 'used' response vector 'y'. Mainly for derived classes where 'y' is transformed, e.g., TranRegression, Regression4TS.

Return the help string that describes the Quality of Fit (QoF) measures provided by the Fit class. Override to correspond to 'fitLabel'.

Return the hyper-parameters.

The log-likelihood function times -2. Override as needed.

An optional reference to an ontological concept

Return the model name including its current hyper-parameter.

Return the mean of squares for error (sse / df._2). Must call diagnose first.

Return the vector of parameter/coefficient values.

Predict the value of 'y = f(z)' by evaluating the formula 'y = Z b',

Predict the value of 'y = f(z)' by evaluating the formula 'y = b dot z', e.g., '(b_0, b_1, b_2) dot (1, z_1, z_2)'.

Given a new discrete data/input vector 'z', predict the 'y'-value of 'f(z)'.

Given the vector 'z', expand it and predict the response value.

Return a basic report on the trained model.

Reset the degrees of freedom to the new updated values. For some models, the degrees of freedom is not known until after the model is built.

Return the vector of residuals/errors.

Return a matrix that is in reverse row order of the given matrix 'a'.

Perform stepwise regression to find the most predictive variables to have in the model, returning the variables left and the new Quality of Fit (QoF) measures for all steps. At each step it calls 'forwardSel' and 'backwardElim' and takes the best of the two actions. Stops when neither action yields improvement.

Compute and return summary diagostics for the regression model.

Produce a summary report with diagnostics for each predictor 'x_j' and the overall quality of fit.

Test the model on the full dataset (i.e., train and evaluate on full dataset).

Train the predictor by fitting the parameter vector (b-vector) in the multiple regression equation

y = b dot x + e = [b_0, ... b_k] dot [1, x_1 , ... x_k] + e

using the ordinary least squares 'OLS' method.

Train a predictive model 'y_ = f(x_) + e' where 'x_' is the data/input matrix and 'y_' is the response/output vector. These arguments default to the full dataset 'x' and 'y', but may be restricted to a training dataset. Training involves estimating the model parameters 'b'. The 'train2' method should work like the 'train' method, but should also optimize hyper-parameters (e.g., shrinkage or learning rate). Only implementing classes needing this capability should implement this method.

Compute the Variance Inflation Factor 'VIF' for each variable to test for multi-collinearity by regressing 'x_j' against the rest of the variables. A VIF over 10 indicates that over 90% of the variance of 'x_j' can be predicted from the other variables, so 'x_j' may be a candidate for removal from the model. Note: override this method to use a superior regression technique.