Extract training fitted values from a psvr model

Returns the length-N in-sample predictions $f(x_k)$ recorded when the model was fitted. No kernel matrix is rebuilt: the values are recovered from state the solver already holds. For the MAPE models that is a matvec against the retained $\Omega$; for the LS-SVR models it is the KKT stationarity identity $$f(x_k) = y_k - (10^{-6} + y_k^2/\Gamma)\,\alpha_k$$ which costs $O(N)$ and holds in both preconditioner branches. The training inputs X are not retained for this purpose.

Usage

# S3 method for class 'psvr_mape'
fitted(object, ...)

# S3 method for class 'psvr_mape_sym'
fitted(object, ...)

# S3 method for class 'psvr_rmspe'
fitted(object, ...)

# S3 method for class 'psvr_rmspe_sym'
fitted(object, ...)

Arguments

object: A fitted object of class "psvr_mape", "psvr_mape_sym", "psvr_rmspe" or "psvr_rmspe_sym", from psvr_mape(), psvr_rmspe(), or a parsnip fit unwrapped with parsnip::extract_fit_engine().
...: Ignored.

Value

Numeric vector of length N (the number of training observations), in training-row order.

Details

The result equals predict(object, X_train) to machine precision. It is not bit-identical: the two use different summation orders (a BLAS matvec versus the column-wise reduction in predict()), and for the LS-SVR models the identity above is exact only up to the residual of the linear solve. Observed agreement is within 3e-12 relative across the four models.

Not reachable through parsnip

parsnip registers neither residuals.model_fit nor fitted.model_fit, so calling either generic on a model_fit dispatches to the stats default and returns NULL silently - no error, no warning. Reach the psvr object first with parsnip::extract_fit_engine(), then call fitted() or residuals() on that. psvr deliberately does not register S3 methods on parsnip's class. Note also that parsnip::augment() recomputes predictions on whatever new_data it is given and reports response residuals only.

Examples

set.seed(1)
X <- matrix(runif(40, 0.5, 3), 20, 2)
y <- 2 + X[, 1]^2
fit <- psvr_rmspe(X, y, kernel = make_kernel("rbf"), gamma = 100)
head(fitted(fit))
#> [1] 3.661072 4.139683 5.812108 8.972789 2.982308 8.699083

# Through parsnip both generics return NULL on the model_fit wrapper;
# extract the engine object first.
df   <- data.frame(x1 = X[, 1], x2 = X[, 2], y = y)
spec <- psvr_rmspe_rbf(cost = 10, rbf_sigma = 0.8)
pfit <- parsnip::fit(spec, y ~ x1 + x2, data = df)
fitted(pfit)                                     # NULL
#> NULL
head(fitted(parsnip::extract_fit_engine(pfit)))  # the fitted values
#> [1] 4.099409 4.155175 5.383078 6.493693 3.322674 6.566849