Produce inference tables for distal causal excursion effects from a
[dcee()] model. By default uses small-sample \(t\)-tests with
df = object$df (subjects minus number of betas). If df
is missing or nonpositive, falls back to large-sample normal (z) inference.
# S3 method for dcee_fit
summary(
object,
lincomb = NULL,
conf_level = 0.95,
show_control_fit = FALSE,
...
)A list of class "summary.dcee_fit" with components:
call — the original call
df — degrees of freedom used for t-tests (may be NA)
conf_level — the confidence level
excursion_effect — data frame with coefficient table for \(\beta\)
lincomb — optional data frame with linear-combination results
control_fit — optional list describing Stage-1 fits (only if show_control_fit)
An object of class "dcee_fit" returned by [dcee()].
Optional numeric vector or matrix specifying linear combinations \(L \beta\). If a vector of length \(p\) (number of betas), a single linear combination is evaluated. If a matrix, it must have \(p\) columns; each row defines one combination. Row names (if present) are used as labels.
Confidence level for intervals (default 0.95).
Logical; if TRUE, include compact information about
the Stage-1 nuisance regressions (if available). When cross_fit = TRUE
in [dcee()], regfit_a0/regfit_a1 refer to the last fold
fit and are provided for inspection only.
Currently ignored.