Fit a genomic prediction model — gs

One interface for several model families: "gblup" (always available), "elastic_net" (glmnet), "random_forest" (ranger), "xgboost" (xgboost), and "ensemble" (a stacked super-learner; see gs_ensemble()). The returned object has a predict() method that takes a new marker matrix.

Usage

gs_fit(y, geno, model = "gblup", ...)

Arguments

y: Numeric phenotype vector (length n), no missing values.
geno: Marker matrix (n x m, coded 0/1/2, no missing values).
model: Model name; see available_models().
...: Model-specific hyperparameters (e.g. alpha for elastic net, num.trees for random forest, nrounds/eta/max_depth for xgboost, base_models for the ensemble).

Value

An object of class gs_model wrapping the fitted model.

Examples

sim <- simulate_population(n = 120, m = 400, seed = 1)
fit <- gs_fit(sim$pheno, sim$geno, model = "gblup")
head(predict(fit, sim$geno))
#> [1] -3.8752143 -5.4217031  0.7654262  6.0236117  5.7933099 -4.5519275