Predict from a BayesR Fit

Description

Computes genomic estimated breeding values (GEBVs) and prediction metrics. Works identically for full-fit, train/test split, and k-fold CV — the caller is responsible for data partitioning; predict() only evaluates the trained model on whatever data it receives.

Usage

## S3 method for class 'masbayes_bayesr'
predict(object, newdata = NULL, y_new = NULL, X_new = NULL, ...)

Arguments

`object`	An object of class `masbayes_bayesr`.
`newdata`	Optional numeric design matrix. If `NULL`, the training GEBVs (`object$pred_train`) are returned.
`y_new`	Optional response vector for `newdata`. When supplied, prediction metrics are computed.
`…`	Unused.

Details

Three usage modes:

In-sample (full-fit): Call predict(fit). Returns training GEBVs and the in-sample metrics already stored in the fit object.
Forecast only: Call predict(fit, W_new) without y_new. Returns GEBVs for the new design matrix; metrics are skipped.
Test evaluation: Call predict(fit, W_test, y_test). Returns test-set GEBVs plus R2, RMSE, accuracy/AUC, bias.

For test sets built with construct_wah_matrix, pass the training matrix structure as reference_structure so that newdata has identical columns to the training matrix.

Value

An object of class masbayes_prediction: a list with GEBV (liability scale for binary), prob (binary only: P(y = 1) = pnorm(GEBV), otherwise NULL), metrics (R2, RMSE, accuracy/AUC, bias or NULL; for binary, computed on the observed/probability scale so bias is the calibration slope), h2, sigma2_g, sigma2_e, variance_components, response_type, model_type, eval_scope, and has_truth.

Examples

library("masbayes")

# Train/test split
fit  <- run_bayesr(W_train, y_train, wtw_train, wty_train,
                   sigma2_e_init = var(y_train)/2,
                   sigma2_ah     = var(y_train)/2)

pred <- predict(fit, W_test, y_test)   # test-set evaluation
pred$metrics$accuracy
pred$metrics$RMSE

fc   <- predict(fit, W_unknown)        # forecast only, no y
fc$GEBV

insamp <- predict(fit)                 # in-sample reference
insamp$metrics$R2