Computes joint epistemic standard errors on the link scale by aggregating across all smooth terms via MC Dropout, capturing cross-term covariance. Parametric model uncertainty (from the linear submodel) is added assuming independence from NN-based epistemic uncertainty.
Computes joint epistemic standard errors on the link scale by aggregating across all smooth terms via MC Dropout, capturing cross-term covariance. Parametric model uncertainty (from the linear submodel) is added assuming independence from NN-based epistemic uncertainty.
.joint_se_eta_mcdropout(ngam, x, forward_passes = 300, verbose = 0).joint_se_eta_mcdropout(ngam, x, forward_passes = 300, verbose = 0)
A numeric vector of length nrow(x) giving epistemic SEs on the link scale.
A numeric vector of length nrow(x) giving epistemic SEs on the link scale.
Fitted neuralGAM object.
New data frame of covariates.
Number of MC Dropout passes (default 300).
Verbosity (0/1).
Ines Ortega-Fernandez, Marta Sestelo
Steps:
Parametric part: mean + variance from linear model.
Nonparametric part: pass-level sums across all smooths.
Joint across-pass variance captures covariance between smooths.
Combined with parametric variance (assumed independent).
Steps:
Parametric part: mean + variance from linear model.
Nonparametric part: pass-level sums across all smooths.
Joint across-pass variance captures covariance between smooths.
Combined with parametric variance (assumed independent).