ZAPC random-forest prediction — Msm329 • rmorie

Formula: Y-hat = 0 when theta-hat > 0.5 and mu-hat otherwise (eq. 15.4): ZAPC_RF converts the probability to a zero rather than to a binary label, and the 0.5 threshold is used because it assumes no prior information.

Usage

Msm329(theta_hat, mu_hat, threshold = 0.5)

Arguments

theta_hat: Estimated probability of an altered zero.
mu_hat: Estimated Poisson rate.
threshold: Decision threshold on theta-hat.

Value

List with estimate, y_hat, is_zero, method.

References

Montesinos Lopez, Montesinos Lopez & Crossa (2022), Multivariate Statistical Machine Learning Methods for Genomic Prediction, Springer, eq. (15.4) p.652. DOI 10.1007/978-3-030-89010-0.

Examples

# classified as a structural zero when theta-hat exceeds the threshold
c(Msm329(theta_hat = 0.7, mu_hat = 2)$estimate, Msm329(theta_hat = 0.2, mu_hat = 2)$estimate)
#> [1] 0 2