Depending on the plot function and input, creates either a 1-dimensional slices, bivariate surface or (1D) cumulative effect.
gg_partial(data, model, term, ..., reference = NULL, ci = TRUE)
gg_partial_ll(
data,
model,
term,
...,
reference = NULL,
ci = FALSE,
time_var = "tend"
)
get_partial_ll(
data,
model,
term,
...,
reference = NULL,
ci = FALSE,
time_var = "tend"
)Data used to fit the model.
A suitable model object which will be used to estimate the
partial effect of term.
A character string indicating the model term for which partial effects should be plotted.
Covariate specifications (expressions) that will be evaluated
by looking for variables in x. Must be of the form z = f(z)
where z is a variable in the data set and f a known
function that can be usefully applied to z. Note that this is also
necessary for single value specifications (e.g. age = c(50)).
For data in PED (piece-wise exponential data) format, one can also specify
the time argument, but see "Details" an "Examples" below.
If specified, should be a list with covariate value pairs,
e.g. list(x1 = 1, x2=50). The calculated partial effect will be relative
to an observation specified in reference.
Logical. Indicates if confidence intervals for the term
of interest should be calculated/plotted. Defaults to TRUE.
The name of the variable that was used in model to
represent follow-up time.