Plot Aggregated SurvSHAP(t) Explanations for Survival Models

This functions plots objects of class aggregated_surv_shap - aggregated time-dependent explanations of survival models created using the model_survshap() function.

# S3 method for aggregated_surv_shap
plot(
  x,
  geom = "importance",
  ...,
  title = "default",
  subtitle = "default",
  max_vars = 7,
  colors = NULL
)

Arguments

x

an object of class aggregated_surv_shap to be plotted

geom

character, one of "importance", "beeswarm", "profile" or "curves". Type of chart to be plotted; "importance" shows the importance of variables over time and aggregated, "beeswarm" shows the distribution of SurvSHAP(t) values for variables and observations, "profile" shows the dependence of SurvSHAP(t) values on variable values, "curves" shows all SurvSHAP(t) curves for selected variable colored by its value or with functional boxplot if boxplot = TRUE.

...

additional parameters passed to internal functions

title

character, title of the plot

subtitle

character, subtitle of the plot, 'default' automatically generates "created for the XXX model (n = YYY)", where XXX is the explainer label and YYY is the number of observations used for calculations

max_vars

maximum number of variables to be plotted (least important variables are ignored), by default 7

colors

character vector containing the colors to be used for plotting variables (containing either hex codes "#FF69B4", or names "blue"). If geom = "importance", the first color will be used for the barplot, the rest for the lines.

Value

An object of the class ggplot.

Plot options

plot.aggregated_surv_shap(geom = "importance")

• rug - character, one of "all", "events", "censors", "none" or NULL. Which times to mark on the x axis in geom_rug().

• rug_colors - character vector containing two colors (containing either hex codes "#FF69B4", or names "blue"). The first color (red by default) will be used to mark event times, whereas the second (grey by default) will be used to mark censor times.

• xlab_left, ylab_right - axis labels for left and right plots (due to different aggregation possibilities)

plot.aggregated_surv_shap(geom = "beeswarm")

• no additional parameters

plot.aggregated_surv_shap(geom = "profile")

• variable - variable for which the profile is to be plotted, by default first from result data

• color_variable - variable used to denote the color, by default equal to variable

plot.aggregated_surv_shap(geom = "curves")

• variable - variable for which SurvSHAP(t) curves are to be plotted, by default first from result data

• boxplot - whether to plot functional boxplot with marked outliers or all curves colored by variable value

• coef - length of the functional boxplot's whiskers as multiple of IQR, by default 1.5

Examples

# \donttest{
veteran <- survival::veteran
rsf_ranger <- ranger::ranger(
    survival::Surv(time, status) ~ .,
    data = veteran,
    respect.unordered.factors = TRUE,
    num.trees = 100,
    mtry = 3,
    max.depth = 5
)
rsf_ranger_exp <- explain(
    rsf_ranger,
    data = veteran[, -c(3, 4)],
    y = survival::Surv(veteran$time, veteran$status),
    verbose = FALSE
)

ranger_global_survshap <- model_survshap(
    explainer = rsf_ranger_exp,
    new_observation = veteran[
        c(1:4, 17:20, 110:113, 126:129),
        !colnames(veteran) %in% c("time", "status")
    ],
    y_true = survival::Surv(
        veteran$time[c(1:4, 17:20, 110:113, 126:129)],
        veteran$status[c(1:4, 17:20, 110:113, 126:129)]
    ),
    aggregation_method = "integral",
    calculation_method = "kernelshap",
)
plot(ranger_global_survshap)

plot(ranger_global_survshap, geom = "beeswarm")

plot(ranger_global_survshap, geom = "profile",
     variable = "age", color_variable = "karno")

plot(ranger_global_survshap, geom = "curves",
     variable = "age")

plot(ranger_global_survshap, geom = "curves",
     variable = "age", boxplot = TRUE)
#> Observations with outlying SurvSHAP(t) values:
#>     trt  celltype karno diagtime age prior
#> 1     1  squamous    60        7  69     0
#> 3     1  squamous    60        3  38     0
#> 18    1 smallcell    40        2  35     0
#> 128   2     large    30        4  39    10

# }