varying_coef: Evaluate the varying coefficient(s) at new t values

In cevcmm: Communication-Efficient Varying Coefficient Mixed-Effects Models

Evaluate the varying coefficient(s) at new t values

Description

For a VCMM with varying coefficients \beta_k(t), k = 1, ..., K, this function evaluates \hat\beta_k(t) at any vector of t_new values, using the same B-spline basis the fit was built on. Useful for diagnostic plots, predictions, and reporting; the Day-13 plot method uses it internally.

Usage

varying_coef(object, t_new, k = NULL, se.fit = FALSE, ...)

Arguments

object	A vcmm_fit object.
t_new	Numeric vector at which to evaluate. Same scale as the original t; the package's normalisation is applied internally.
k	Integer vector of which varying coefficients to evaluate (1-based). Default NULL = all K.
se.fit	Logical. If TRUE, also returns pointwise standard errors.
...	Unused.

Details

The constant intercept \hat\beta_0 is not returned (it does not vary in t); use fixef(fit)\$intercept for that.

Pointwise standard errors are available via se.fit = TRUE, computed as

\mathrm{SE}(\hat\beta_k(t)) = \sqrt{B(t)^\top \widehat{\mathrm{Var}}(\beta_{(k)})\, B(t)}

where \beta_{(k)} is the basis-coefficient sub-vector for coefficient k and the covariance comes from vcov(object, which = "beta").

Value

Either a numeric matrix (length(t_new) by length(k), default), or a list with components fit and se.fit when se.fit = TRUE.

See Also

fixef.vcmm_fit, coef.vcmm_fit.

cevcmm documentation built on July 24, 2026, 5:07 p.m.