Plot 1D (smooth) effects — gg

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 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`.

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))