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
Data used to fit the

`model`

.- model
A suitable model object which will be used to estimate the partial effect of

`term`

.- 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.- reference
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`

.- ci
Logical. Indicates if confidence intervals for the

`term`

of interest should be calculated/plotted. Defaults to`TRUE`

.- time_var
The name of the variable that was used in

`model`

to represent follow-up time.