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.
Arguments
- object
A
vcmm_fitobject.- 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= allK.- se.fit
Logical. If
TRUE, also returns pointwise standard errors.- ...
Unused.
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.
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").