Plot 1D (smooth) effects

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))
#> # A tibble: 40 × 14
#>    tstart  tend intlen interval    id offset ped_status charlson_score   age
#>     <dbl> <dbl>  <dbl> <fct>    <dbl>  <dbl>      <dbl>          <dbl> <dbl>
#>  1      0     5      5 (0,5]     101.   1.61          0           2.71  16  
#>  2      0     5      5 (0,5]     101.   1.61          0           2.71  19.9
#>  3      0     5      5 (0,5]     101.   1.61          0           2.71  23.8
#>  4      0     5      5 (0,5]     101.   1.61          0           2.71  27.7
#>  5      0     5      5 (0,5]     101.   1.61          0           2.71  31.6
#>  6      0     5      5 (0,5]     101.   1.61          0           2.71  35.5
#>  7      0     5      5 (0,5]     101.   1.61          0           2.71  39.4
#>  8      0     5      5 (0,5]     101.   1.61          0           2.71  43.3
#>  9      0     5      5 (0,5]     101.   1.61          0           2.71  47.2
#> 10      0     5      5 (0,5]     101.   1.61          0           2.71  51.1
#> # ℹ 30 more rows
#> # ℹ 5 more variables: sex <fct>, transfusion <fct>, complications <chr>,
#> #   metastases <fct>, resection <fct>
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))