Flexible, high-level plotting function for (non-linear) effects conditional on further covariate specifications and potentially relative to a comparison specification.
gg_slice(data, model, term, ..., reference = NULL, ci = TRUE)Arguments
- 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 formz = f(z)wherezis a variable in the data set andfa known function that can be usefully applied toz. 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 inreference.- ci
Logical. Indicates if confidence intervals for the
termof interest should be calculated/plotted. Defaults toTRUE.
Examples
ped <- tumor[1:200, ] %>% as_ped(Surv(days, status) ~ . )
model <- mgcv::gam(ped_status~s(tend) + s(age, by = complications), data=ped,
family = poisson(), offset=offset)
make_newdata(ped, age = seq_range(age, 20), complications = levels(complications))
#> tstart tend intlen interval id offset ped_status charlson_score
#> 1 0 5 5 (0,5] 100.5648 1.609438 0 2.709355
#> 2 0 5 5 (0,5] 100.5648 1.609438 0 2.709355
#> 3 0 5 5 (0,5] 100.5648 1.609438 0 2.709355
#> 4 0 5 5 (0,5] 100.5648 1.609438 0 2.709355
#> 5 0 5 5 (0,5] 100.5648 1.609438 0 2.709355
#> 6 0 5 5 (0,5] 100.5648 1.609438 0 2.709355
#> 7 0 5 5 (0,5] 100.5648 1.609438 0 2.709355
#> 8 0 5 5 (0,5] 100.5648 1.609438 0 2.709355
#> 9 0 5 5 (0,5] 100.5648 1.609438 0 2.709355
#> 10 0 5 5 (0,5] 100.5648 1.609438 0 2.709355
#> 11 0 5 5 (0,5] 100.5648 1.609438 0 2.709355
#> 12 0 5 5 (0,5] 100.5648 1.609438 0 2.709355
#> 13 0 5 5 (0,5] 100.5648 1.609438 0 2.709355
#> 14 0 5 5 (0,5] 100.5648 1.609438 0 2.709355
#> 15 0 5 5 (0,5] 100.5648 1.609438 0 2.709355
#> 16 0 5 5 (0,5] 100.5648 1.609438 0 2.709355
#> 17 0 5 5 (0,5] 100.5648 1.609438 0 2.709355
#> 18 0 5 5 (0,5] 100.5648 1.609438 0 2.709355
#> 19 0 5 5 (0,5] 100.5648 1.609438 0 2.709355
#> 20 0 5 5 (0,5] 100.5648 1.609438 0 2.709355
#> 21 0 5 5 (0,5] 100.5648 1.609438 0 2.709355
#> 22 0 5 5 (0,5] 100.5648 1.609438 0 2.709355
#> 23 0 5 5 (0,5] 100.5648 1.609438 0 2.709355
#> 24 0 5 5 (0,5] 100.5648 1.609438 0 2.709355
#> 25 0 5 5 (0,5] 100.5648 1.609438 0 2.709355
#> 26 0 5 5 (0,5] 100.5648 1.609438 0 2.709355
#> 27 0 5 5 (0,5] 100.5648 1.609438 0 2.709355
#> 28 0 5 5 (0,5] 100.5648 1.609438 0 2.709355
#> 29 0 5 5 (0,5] 100.5648 1.609438 0 2.709355
#> 30 0 5 5 (0,5] 100.5648 1.609438 0 2.709355
#> 31 0 5 5 (0,5] 100.5648 1.609438 0 2.709355
#> 32 0 5 5 (0,5] 100.5648 1.609438 0 2.709355
#> 33 0 5 5 (0,5] 100.5648 1.609438 0 2.709355
#> 34 0 5 5 (0,5] 100.5648 1.609438 0 2.709355
#> 35 0 5 5 (0,5] 100.5648 1.609438 0 2.709355
#> 36 0 5 5 (0,5] 100.5648 1.609438 0 2.709355
#> 37 0 5 5 (0,5] 100.5648 1.609438 0 2.709355
#> 38 0 5 5 (0,5] 100.5648 1.609438 0 2.709355
#> 39 0 5 5 (0,5] 100.5648 1.609438 0 2.709355
#> 40 0 5 5 (0,5] 100.5648 1.609438 0 2.709355
#> age sex transfusion complications metastases resection
#> 1 16.00000 male no no yes no
#> 2 19.89474 male no no yes no
#> 3 23.78947 male no no yes no
#> 4 27.68421 male no no yes no
#> 5 31.57895 male no no yes no
#> 6 35.47368 male no no yes no
#> 7 39.36842 male no no yes no
#> 8 43.26316 male no no yes no
#> 9 47.15789 male no no yes no
#> 10 51.05263 male no no yes no
#> 11 54.94737 male no no yes no
#> 12 58.84211 male no no yes no
#> 13 62.73684 male no no yes no
#> 14 66.63158 male no no yes no
#> 15 70.52632 male no no yes no
#> 16 74.42105 male no no yes no
#> 17 78.31579 male no no yes no
#> 18 82.21053 male no no yes no
#> 19 86.10526 male no no yes no
#> 20 90.00000 male no no yes no
#> 21 16.00000 male no yes yes no
#> 22 19.89474 male no yes yes no
#> 23 23.78947 male no yes yes no
#> 24 27.68421 male no yes yes no
#> 25 31.57895 male no yes yes no
#> 26 35.47368 male no yes yes no
#> 27 39.36842 male no yes yes no
#> 28 43.26316 male no yes yes no
#> 29 47.15789 male no yes yes no
#> 30 51.05263 male no yes yes no
#> 31 54.94737 male no yes yes no
#> 32 58.84211 male no yes yes no
#> 33 62.73684 male no yes yes no
#> 34 66.63158 male no yes yes no
#> 35 70.52632 male no yes yes no
#> 36 74.42105 male no yes yes no
#> 37 78.31579 male no yes yes no
#> 38 82.21053 male no yes yes no
#> 39 86.10526 male no yes yes no
#> 40 90.00000 male no yes yes no
gg_slice(ped, model, "age", age=seq_range(age, 20), complications=levels(complications))
gg_slice(ped, model, "age", age=seq_range(age, 20), complications=levels(complications),
ci = FALSE)
gg_slice(ped, model, "age", age=seq_range(age, 20), complications=levels(complications),
reference=list(age = 50))
