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))