Plot 2-Dimensional Model Profile for Survival Models

This function plots objects of class "model_profile_2d_survival" created using the model_profile_2d() function.

# S3 method for model_profile_2d_survival
plot(
  x,
  ...,
  variables = NULL,
  times = NULL,
  marginalize_over_time = FALSE,
  facet_ncol = NULL,
  title = "default",
  subtitle = "default",
  colors = NULL
)

Arguments

x

an object of class model_profile_2d_survival to be plotted

...

additional objects of class model_profile_2d_survival to be plotted together

variables

list of character vectors of length 2, names of pairs of variables to be plotted

times

numeric vector, times for which the profile should be plotted, the times must be present in the 'times' field of the explainer. If NULL (default) then the median survival time (if available) or the median time from the explainer object is used.

marginalize_over_time

logical, if TRUE then the profile is calculated for all times and then averaged over time, if FALSE (default) then the profile is calculated for each time separately

facet_ncol

number of columns for arranging subplots

title

character, title of the plot. 'default' automatically generates either "2D partial dependence survival profiles" or "2D accumulated local effects survival profiles" depending on the explanation type.

subtitle

character, subtitle of the plot, 'default' automatically generates "created for the XXX model", where XXX is the explainer labels, if marginalize_over_time = FALSE, time is also added to the subtitle

colors

character vector containing the colors to be used for plotting variables (containing either hex codes "#FF69B4", or names "blue")

Value

A collection of ggplot objects arranged with the patchwork package.

Examples

# \donttest{
library(survival)
library(survex)

cph <- coxph(Surv(time, status) ~ ., data = veteran, model = TRUE, x = TRUE, y = TRUE)
cph_exp <- explain(cph)
#> Preparation of a new explainer is initiated 
#>   -> model label       :  coxph (  default  ) 
#>   -> data              :  137  rows  6  cols (  extracted from the model  ) 
#>   -> target variable   :  137  values ( 128 events and 9 censored , censoring rate = 0.066 ) (  extracted from the model  ) 
#>   -> times             :  50 unique time points , min = 1.5 , median survival time = 80 , max = 999 
#>   -> times             :  (  generated from y as uniformly distributed survival quantiles based on Kaplan-Meier estimator  ) 
#>   -> predict function  :  predict.coxph with type = 'risk' will be used (  default  ) 
#>   -> predict survival function  :  predictSurvProb.coxph will be used (  default  ) 
#>   -> predict cumulative hazard function  :  -log(predict_survival_function) will be used (  default  ) 
#>   -> model_info        :  package survival , ver. 3.5.8 , task survival (  default  ) 
#>   A new explainer has been created!  

cph_model_profile_2d <- model_profile_2d(cph_exp,
    variables = list(
        c("age", "celltype"),
        c("age", "karno")
    )
)
head(cph_model_profile_2d$result)
#>   _v1name_ _v2name_  _v1type_    _v2type_        _v1value_ _v2value_ _times_
#> 1      age celltype numerical categorical               34     adeno     1.5
#> 2      age celltype numerical categorical 35.9583333333333     adeno     1.5
#> 3      age celltype numerical categorical 37.9166666666667     adeno     1.5
#> 4      age celltype numerical categorical           39.875     adeno     1.5
#> 5      age celltype numerical categorical 41.8333333333333     adeno     1.5
#> 6      age celltype numerical categorical 43.7916666666667     adeno     1.5
#>   _label_    _yhat_
#> 1   coxph 0.9711210
#> 2   coxph 0.9715980
#> 3   coxph 0.9720673
#> 4   coxph 0.9725290
#> 5   coxph 0.9729833
#> 6   coxph 0.9734302
plot(cph_model_profile_2d, variables = list(c("age", "celltype")), times = cph_exp$times[20])


cph_model_profile_2d_ale <- model_profile_2d(cph_exp,
    variables = list(c("age", "karno")),
    type = "accumulated"
)
head(cph_model_profile_2d_ale$result)
#>   _v1name_ _v2name_  _v1type_  _v2type_ _v1value_ _v2value_ _times_    _yhat_
#> 1      age    karno numerical numerical        34        10     1.5 0.9821661
#> 2      age    karno numerical numerical        34        10     4.0 0.9562840
#> 3      age    karno numerical numerical        34        10     7.0 0.9310471
#> 4      age    karno numerical numerical        34        10     8.0 0.8984986
#> 5      age    karno numerical numerical        34        10    10.0 0.8828059
#> 6      age    karno numerical numerical        34        10    12.0 0.8602254
#>   _right_ _left_ _top_ _bottom_ _count_ _label_
#> 1      36     34    10       15       0   coxph
#> 2      36     34    10       15       0   coxph
#> 3      36     34    10       15       0   coxph
#> 4      36     34    10       15       0   coxph
#> 5      36     34    10       15       0   coxph
#> 6      36     34    10       15       0   coxph
plot(cph_model_profile_2d_ale, times = cph_exp$times[c(10, 20)], marginalize_over_time = TRUE)

# }