Dataset Level Variable Profile as Partial Dependence Explanations for Survival Models

This function calculates explanations on a dataset level that help explore model response as a function of selected variables. The explanations are calculated as an extension of Partial Dependence Profiles with the inclusion of the time dimension.

model_profile(
  explainer,
  variables = NULL,
  N = 100,
  ...,
  groups = NULL,
  k = NULL,
  type = "partial",
  center = FALSE,
  output_type = "survival"
)

# S3 method for surv_explainer
model_profile(
  explainer,
  variables = NULL,
  N = 100,
  ...,
  categorical_variables = NULL,
  grid_points = 51,
  variable_splits_type = "uniform",
  groups = NULL,
  k = NULL,
  center = FALSE,
  type = "partial",
  output_type = "survival"
)

Arguments

explainer: an explainer object - model preprocessed by the explain() function
variables: character, a vector of names of variables to be explained
N: number of observations used for the calculation of aggregated profiles. By default 100. If NULL all observations are used.
...: other parameters passed to DALEX::model_profile if output_type == "risk", otherwise ignored
groups: if output_type == "risk" a variable name that will be used for grouping. By default NULL, so no groups are calculated. If output_type == "survival" then ignored
k: passed to DALEX::model_profile if output_type == "risk", otherwise ignored
type: the type of variable profile, "partial" for Partial Dependence, "accumulated" for Accumulated Local Effects, or "conditional" (available only for output_type == "risk")
center: logical, should profiles be centered around the average prediction
output_type: either "survival", "chf" or "risk" the type of survival model output that should be considered for explanations. If "survival" the explanations are based on the survival function. If "chf" the explanations are based on the cumulative hazard function. Otherwise the scalar risk predictions are used by the DALEX::predict_profile function.
categorical_variables: character, a vector of names of additional variables which should be treated as categorical (factors are automatically treated as categorical variables). If it contains variable names not present in the variables argument, they will be added at the end.
grid_points: maximum number of points for profile calculations. Note that the final number of points may be lower than grid_points. Will be passed to internal function. By default 51.
variable_splits_type: character, decides how variable grids should be calculated. Use "quantiles" for percentiles or "uniform" (default) to get uniform grid of points.

Value

An object of class model_profile_survival. It is a list with the element result containing the results of the calculation.

Examples

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

cph <- coxph(Surv(time, status) ~ ., data = veteran, model = TRUE, x = TRUE, y = TRUE)
rsf_src <- randomForestSRC::rfsrc(Surv(time, status) ~ ., data = veteran)

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!  
rsf_src_exp <- explain(rsf_src)
#> Preparation of a new explainer is initiated 
#>   -> model label       :  rfsrc (  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  :  sum over the predict_cumulative_hazard_function will be used (  default  ) 
#>   -> predict survival function  :  stepfun based on predict.rfsrc()$survival will be used (  default  ) 
#>   -> predict cumulative hazard function  :  stepfun based on predict.rfsrc()$chf will be used (  default  ) 
#>   -> model_info        :  package randomForestSRC , ver. 3.2.3 , task survival (  default  ) 
#>   A new explainer has been created!  

cph_model_profile <- model_profile(cph_exp,
    output_type = "survival",
    variables = c("age")
)

head(cph_model_profile$result)
#>   _vname_   _vtype_ _label_   _x_ _times_    _yhat_ _ids_
#> 1     age numerical   coxph 34.00     1.5 0.9840125     0
#> 2     age numerical   coxph 34.94     1.5 0.9841410     0
#> 3     age numerical   coxph 35.00     1.5 0.9841492     0
#> 4     age numerical   coxph 35.88     1.5 0.9842684     0
#> 5     age numerical   coxph 36.82     1.5 0.9843949     0
#> 6     age numerical   coxph 37.00     1.5 0.9844190     0

plot(cph_model_profile)


rsf_model_profile <- model_profile(rsf_src_exp,
    output_type = "survival",
    variables = c("age", "celltype"),
    type = "accumulated"
)

head(rsf_model_profile$result)
#>   _vname_   _vtype_ _label_ _x_ _times_    _yhat_ _ids_
#> 1     age numerical   rfsrc  34     1.5 0.8541533     0
#> 2     age numerical   rfsrc  34     4.0 0.7929659     0
#> 3     age numerical   rfsrc  34     7.0 0.7756136     0
#> 4     age numerical   rfsrc  34     8.0 0.7516203     0
#> 5     age numerical   rfsrc  34    10.0 0.7406234     0
#> 6     age numerical   rfsrc  34    12.0 0.7260259     0

plot(rsf_model_profile, variables = c("age", "celltype"), numerical_plot_type = "contours")

# }