[1] Brier, Glenn W. "Verification of forecasts expressed in terms of probability." Monthly Weather Review 78.1 (1950): 1-3.
[2] Graf, Erika, et al. "Assessment and comparison of prognostic classification schemes for survival data." Statistics in Medicine 18.17‐18 (1999): 2529-2545.
[1] Brier, Glenn W. "Verification of forecasts expressed in terms of probability." Monthly Weather Review 78.1 (1950): 1-3.
+[2] Graf, Erika, et al. "Assessment and comparison of prognostic classification schemes for survival data." Statistics in Medicine 18.17‐18 (1999): 2529-2545.
See also
diff --git a/reference/c_index.html b/reference/c_index.html index 47c9020e..c745487e 100644 --- a/reference/c_index.html +++ b/reference/c_index.html @@ -25,7 +25,7 @@Value
References
-[1] Harrell, F.E., Jr., et al. "Regression modelling strategies for improved prognostic prediction." Statistics in Medicine 3.2 (1984): 143-152.
+[1] Harrell, F.E., Jr., et al. "Regression modelling strategies for improved prognostic prediction." Statistics in Medicine 3.2 (1984): 143-152.
[1] Uno, Hajime, et al. "Evaluating prediction rules for t-year survivors with censored regression models." Journal of the American Statistical Association 102.478 (2007): 527-537.
-[2] Hung, Hung, and Chin‐Tsang Chiang. "Optimal composite markers for time dependent receiver operating characteristic curves with censored survival data." Scandinavian Journal of Statistics 37.4 (2010): 664-679.
+[1] Uno, Hajime, et al. "Evaluating prediction rules for t-year survivors with censored regression models." Journal of the American Statistical Association 102.478 (2007): 527-537.
+[2] Hung, Hung, and Chin‐Tsang Chiang. "Optimal composite markers for time dependent receiver operating characteristic curves with censored survival data." Scandinavian Journal of Statistics 37.4 (2010): 664-679.
[1] Brier, Glenn W. "Verification of forecasts expressed in terms of probability." Monthly Weather Review 78.1 (1950): 1-3.
-[2] Graf, Erika, et al. "Assessment and comparison of prognostic classification schemes for survival data." Statistics in Medicine 18.17‐18 (1999): 2529-2545.
+[1] Brier, Glenn W. "Verification of forecasts expressed in terms of probability." Monthly Weather Review 78.1 (1950): 1-3.
+[2] Graf, Erika, et al. "Assessment and comparison of prognostic classification schemes for survival data." Statistics in Medicine 18.17‐18 (1999): 2529-2545.
[1] Uno, Hajime, et al. "Evaluating prediction rules for t-year survivors with censored regression models." Journal of the American Statistical Association 102.478 (2007): 527-537.
-[2] Hung, Hung, and Chin‐Tsang Chiang. "Optimal composite markers for time‐dependent receiver operating characteristic curves with censored survival data." Scandinavian Journal of Statistics 37.4 (2010): 664-679.
+[1] Uno, Hajime, et al. "Evaluating prediction rules for t-year survivors with censored regression models." Journal of the American Statistical Association 102.478 (2007): 527-537.
+[2] Hung, Hung, and Chin‐Tsang Chiang. "Optimal composite markers for time‐dependent receiver operating characteristic curves with censored survival data." Scandinavian Journal of Statistics 37.4 (2010): 664-679.
[1] Graf, Erika, et al. "Assessment and comparison of prognostic classification schemes for survival data." Statistics in Medicine 18.17‐18 (1999): 2529-2545.
+[1] Graf, Erika, et al. "Assessment and comparison of prognostic classification schemes for survival data." Statistics in Medicine 18.17‐18 (1999): 2529-2545.
[1] Harrell, F.E., Jr., et al. "Regression modelling strategies for improved prognostic prediction." Statistics in Medicine 3.2 (1984): 143-152.
+[1] Harrell, F.E., Jr., et al. "Regression modelling strategies for improved prognostic prediction." Statistics in Medicine 3.2 (1984): 143-152.
[1] Uno, Hajime, et al. "Evaluating prediction rules for t-year survivors with censored regression models." Journal of the American Statistical Association 102.478 (2007): 527-537.
-[2] Hung, Hung, and Chin‐Tsang Chiang. "Optimal composite markers for time‐dependent receiver operating characteristic curves with censored survival data." Scandinavian Journal of Statistics 37.4 (2010): 664-679.
+[1] Uno, Hajime, et al. "Evaluating prediction rules for t-year survivors with censored regression models." Journal of the American Statistical Association 102.478 (2007): 527-537.
+[2] Hung, Hung, and Chin‐Tsang Chiang. "Optimal composite markers for time‐dependent receiver operating characteristic curves with censored survival data." Scandinavian Journal of Statistics 37.4 (2010): 664-679.
[1] Uno, Hajime, et al. "Evaluating prediction rules for t-year survivors with censored regression models." Journal of the American Statistical Association 102.478 (2007): 527-537.
-[2] Hung, Hung, and Chin‐Tsang Chiang. "Optimal composite markers for time‐dependent receiver operating characteristic curves with censored survival data." Scandinavian Journal of Statistics 37.4 (2010): 664-679.
+[1] Uno, Hajime, et al. "Evaluating prediction rules for t-year survivors with censored regression models." Journal of the American Statistical Association 102.478 (2007): 527-537.
+[2] Hung, Hung, and Chin‐Tsang Chiang. "Optimal composite markers for time‐dependent receiver operating characteristic curves with censored survival data." Scandinavian Journal of Statistics 37.4 (2010): 664-679.
Harrell's concordance index [1]
-- -
C/D AUC using the estimator proposed by Uno et. al [4]
+Harrell's concordance index [1]
+Brier score [2, 3]
+C/D AUC using the estimator proposed by Uno et. al [4]
integral of the Brier score
integral of the C/D AUC
[1] Harrell, F.E., Jr., et al. "Regression modelling strategies for improved prognostic prediction." Statistics in Medicine 3.2 (1984): 143-152.
-[2] Brier, Glenn W. "Verification of forecasts expressed in terms of probability." Monthly Weather Review 78.1 (1950): 1-3.
-[3] Graf, Erika, et al. "Assessment and comparison of prognostic classification schemes for survival data." Statistics in Medicine 18.17‐18 (1999): 2529-2545.
-[4] Uno, Hajime, et al. "Evaluating prediction rules for t-year survivors with censored regression models." Journal of the American Statistical Association 102.478 (2007): 527-537.
+[1] Harrell, F.E., Jr., et al. "Regression modelling strategies for improved prognostic prediction." Statistics in Medicine 3.2 (1984): 143-152.
+[2] Brier, Glenn W. "Verification of forecasts expressed in terms of probability." Monthly Weather Review 78.1 (1950): 1-3.
+[3] Graf, Erika, et al. "Assessment and comparison of prognostic classification schemes for survival data." Statistics in Medicine 18.17‐18 (1999): 2529-2545.
+[4] Uno, Hajime, et al. "Evaluating prediction rules for t-year survivors with censored regression models." Journal of the American Statistical Association 102.478 (2007): 527-537.
a two element numeric vector or matrix of one row and two columns, the first element being the true observed time and the second the status of the observation, used for plotting
+- N +
a positive integer, number of observations used as the background data
+
+
- calculation_method -
a character, either
"kernelshap"for use ofkernelshaplibrary (providing faster Kernel SHAP with refinements) or"exact_kernel"for exact Kernel SHAP estimation
+a character, either
"kernelshap"for use ofkernelshaplibrary (providing faster Kernel SHAP with refinements),"exact_kernel"for exact Kernel SHAP estimation, or"treeshap"for use oftreeshaplibrary (efficient implementation to compute SHAP values for tree-based models).- aggregation_method diff --git a/reference/plot.aggregated_surv_shap-1.png b/reference/plot.aggregated_surv_shap-1.png index 6810475f..ffd5ed20 100644 Binary files a/reference/plot.aggregated_surv_shap-1.png and b/reference/plot.aggregated_surv_shap-1.png differ diff --git a/reference/plot.aggregated_surv_shap-2.png b/reference/plot.aggregated_surv_shap-2.png index ea4e4640..145bfd36 100644 Binary files a/reference/plot.aggregated_surv_shap-2.png and b/reference/plot.aggregated_surv_shap-2.png differ diff --git a/reference/plot.aggregated_surv_shap-3.png b/reference/plot.aggregated_surv_shap-3.png index 394cb53b..b45f95ec 100644 Binary files a/reference/plot.aggregated_surv_shap-3.png and b/reference/plot.aggregated_surv_shap-3.png differ diff --git a/reference/plot.aggregated_surv_shap-4.png b/reference/plot.aggregated_surv_shap-4.png index bed469df..73f9fb53 100644 Binary files a/reference/plot.aggregated_surv_shap-4.png and b/reference/plot.aggregated_surv_shap-4.png differ diff --git a/reference/plot.aggregated_surv_shap-5.png b/reference/plot.aggregated_surv_shap-5.png index 5b134443..59229986 100644 Binary files a/reference/plot.aggregated_surv_shap-5.png and b/reference/plot.aggregated_surv_shap-5.png differ diff --git a/reference/plot.aggregated_surv_shap.html b/reference/plot.aggregated_surv_shap.html index 505c78f1..53c60c7b 100644 --- a/reference/plot.aggregated_surv_shap.html +++ b/reference/plot.aggregated_surv_shap.html @@ -26,7 +26,7 @@ @@ -156,8 +156,14 @@
variable- variable for which the profile is to be plotted, by default first from result datacolor_variable- variable used to denote the color, by default equal tovariable
-variable- variable for which SurvSHAP(t) curves are to be plotted, by default first from result data
+variable- variable for which SurvSHAP(t) curves are to be plotted, by default first from result databoxplot- 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- N -
the maximum number of observations used for calculation of attributions. If
NULL(default) all observations will be used.
+the number of observations used for calculation of attributions. If
NULL(default) all explainer data will be used for SurvSHAP(t) and 100 neigbours for SurvLIME.- type @@ -120,7 +120,7 @@
- output_type -
either
"survival"or"risk"the type of survival model output that should be considered for explanations. If"survival"the explanations are based on the survival function. Otherwise the scalar risk predictions are used by theDALEX::predict_partsfunction.
+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 theDALEX::predict_partsfunction.- explanation_label @@ -147,8 +147,7 @@
categorical_variables- character vector, names of variables that should be treated as categories (factors are included by default)k- a small positive number > 1, added to chf before taking log, so that weigths aren't negativefor
survshaptimestamps- a numeric vector, time points at which the survival function will be evaluated
-y_true- a two element numeric vector or matrix of one row and two columns, the first element being the true observed time and the second the status of the observation, used for plotting
+for
survshapy_true- a two element numeric vector or matrix of one row and two columns, the first element being the true observed time and the second the status of the observation, used for plottingcalculation_method- a character, either"kernelshap"for use ofkernelshaplibrary (providing faster Kernel SHAP with refinements) or"exact_kernel"for exact Kernel SHAP estimationaggregation_method- a character, either"mean_absolute"or"integral","max_absolute","sum_of_squares"
@@ -206,10 +205,10 @@ additional parameters, passed to internal functions
+- N +
a positive integer, number of observations used as the background data
+
+
- y_true
a two element numeric vector or matrix of one row and two columns, the first element being the true observed time and the second the status of the observation, used for plotting
- calculation_method -
a character, either
"kernelshap"for use ofkernelshaplibrary (providing faster Kernel SHAP with refinements) or"exact_kernel"for exact Kernel SHAP estimation
+a character, either
"kernelshap"for use ofkernelshaplibrary (providing faster Kernel SHAP with refinements),"exact_kernel"for exact Kernel SHAP estimation, or"treeshap"for use oftreeshaplibrary (efficient implementation to compute SHAP values for tree-based models).- aggregation_method diff --git a/reference/survival_to_cumulative_hazard.html b/reference/survival_to_cumulative_hazard.html index 8463132f..9dc82f26 100644 --- a/reference/survival_to_cumulative_hazard.html +++ b/reference/survival_to_cumulative_hazard.html @@ -25,7 +25,7 @@ diff --git a/reference/theme_survex-1.png b/reference/theme_survex-1.png index 0677af53..09f28e52 100644 Binary files a/reference/theme_survex-1.png and b/reference/theme_survex-1.png differ diff --git a/reference/theme_survex-2.png b/reference/theme_survex-2.png index 4b6f9632..3d297785 100644 Binary files a/reference/theme_survex-2.png and b/reference/theme_survex-2.png differ diff --git a/reference/theme_survex.html b/reference/theme_survex.html index ea53d33f..64e2ac1f 100644 --- a/reference/theme_survex.html +++ b/reference/theme_survex.html @@ -25,7 +25,7 @@ diff --git a/reference/transform_to_stepfunction.html b/reference/transform_to_stepfunction.html index 7f09d083..97d40801 100644 --- a/reference/transform_to_stepfunction.html +++ b/reference/transform_to_stepfunction.html @@ -25,7 +25,7 @@
Examples
type = "survlime" ) head(cph_predict_parts_survlime$result) -#> trt karno diagtime age prior celltypelarge -#> 1 -0.03469248 -0.001903851 -5.022977e-05 0.002085759 0.0005119294 0.00931774 -#> celltypesmallcell celltypesquamous -#> 1 0.02788239 -0.002751905 +#> trt karno diagtime age prior +#> 1 -0.002017738 -0.0009651749 -0.0001565814 0.0007578033 0.001391971 +#> celltypelarge celltypesmallcell celltypesquamous +#> 1 -0.03618306 -0.002838358 -0.01624022 plot(cph_predict_parts_survlime, type = "local_importance")
# }
diff --git a/reference/predict_profile.surv_explainer.html b/reference/predict_profile.surv_explainer.html
index 4c50af33..08008b7e 100644
--- a/reference/predict_profile.surv_explainer.html
+++ b/reference/predict_profile.surv_explainer.html
@@ -25,7 +25,7 @@
diff --git a/reference/risk_from_chf.html b/reference/risk_from_chf.html
index bca34c00..718c6939 100644
--- a/reference/risk_from_chf.html
+++ b/reference/risk_from_chf.html
@@ -25,7 +25,7 @@
diff --git a/reference/surv_ceteris_paribus.html b/reference/surv_ceteris_paribus.html
index 67f3110f..caeb0dad 100644
--- a/reference/surv_ceteris_paribus.html
+++ b/reference/surv_ceteris_paribus.html
@@ -26,7 +26,7 @@
diff --git a/reference/surv_feature_importance.html b/reference/surv_feature_importance.html
index 28bf7c98..00583458 100644
--- a/reference/surv_feature_importance.html
+++ b/reference/surv_feature_importance.html
@@ -27,7 +27,7 @@
diff --git a/reference/surv_integrated_feature_importance.html b/reference/surv_integrated_feature_importance.html
index 5c023ef8..71493c97 100644
--- a/reference/surv_integrated_feature_importance.html
+++ b/reference/surv_integrated_feature_importance.html
@@ -27,7 +27,7 @@
diff --git a/reference/surv_lime.html b/reference/surv_lime.html
index 3f804258..7ece1d11 100644
--- a/reference/surv_lime.html
+++ b/reference/surv_lime.html
@@ -25,7 +25,7 @@
diff --git a/reference/surv_model_info.html b/reference/surv_model_info.html
index 0b7a5300..13e57345 100644
--- a/reference/surv_model_info.html
+++ b/reference/surv_model_info.html
@@ -27,7 +27,7 @@
diff --git a/reference/surv_model_performance.html b/reference/surv_model_performance.html
index cbb0e993..05fc611c 100644
--- a/reference/surv_model_performance.html
+++ b/reference/surv_model_performance.html
@@ -25,7 +25,7 @@
diff --git a/reference/surv_shap.html b/reference/surv_shap.html
index 595f4fae..519bda78 100644
--- a/reference/surv_shap.html
+++ b/reference/surv_shap.html
@@ -25,7 +25,7 @@
@@ -88,9 +88,10 @@ Helper functions for
new_observation, output_type, ..., + N = NULL, y_true = NULL, - calculation_method = "kernelshap", - aggregation_method = "integral" + calculation_method = c("kernelshap", "exact_kernel", "treeshap"), + aggregation_method = c("integral", "mean_absolute", "max_absolute", "sum_of_squares") ) @@ -112,12 +113,16 @@predict_parts.RArguments
See also
diff --git a/reference/cd_auc.html b/reference/cd_auc.html index 69abe9f6..7b18d0e5 100644 --- a/reference/cd_auc.html +++ b/reference/cd_auc.html @@ -27,7 +27,7 @@Calculate Cumulative/Dynamic AUC
This function calculates the Cumulative/Dynamic AUC metric for a survival model. It is done using the -estimator proposed proposed by Uno et al. [1], -and Hung and Chang [2].
+estimator proposed proposed by Uno et al. [1], +and Hung and Chang [2].Details
References
-See also
diff --git a/reference/cumulative_hazard_to_survival.html b/reference/cumulative_hazard_to_survival.html index a6c6f702..70e3c8c3 100644 --- a/reference/cumulative_hazard_to_survival.html +++ b/reference/cumulative_hazard_to_survival.html @@ -25,7 +25,7 @@Details
References
-See also
diff --git a/reference/integrated_cd_auc.html b/reference/integrated_cd_auc.html index 3e5e5353..a5d2f0e9 100644 --- a/reference/integrated_cd_auc.html +++ b/reference/integrated_cd_auc.html @@ -25,7 +25,7 @@Value
numeric from 0 to 1, higher values indicate better performance
-#' @section References:
#' @section References:
Details
diff --git a/reference/loss_adapt_mlr3proba.html b/reference/loss_adapt_mlr3proba.html index 79d0e0ee..b8f4b9e4 100644 --- a/reference/loss_adapt_mlr3proba.html +++ b/reference/loss_adapt_mlr3proba.html @@ -25,7 +25,7 @@Value
a function with standardized parameters (y_true, risk, surv, times) that can be used to calculate loss
if(FALSE) -measure <- msr("surv.calib_beta") -mlr_measure <- loss_adapt_mlr3proba(measure)
- - +Examples
+if(FALSE){
+ measure <- msr("surv.calib_beta")
+ mlr_measure <- loss_adapt_mlr3proba(measure)
+}
+
+Value
References
-Value
References
-See also
diff --git a/reference/loss_one_minus_cd_auc.html b/reference/loss_one_minus_cd_auc.html index 4ccf7223..3684aa96 100644 --- a/reference/loss_one_minus_cd_auc.html +++ b/reference/loss_one_minus_cd_auc.html @@ -25,7 +25,7 @@Value
a numeric vector of length equal to the length of the times vector, each value (from the range from 0 to 1) represents 1 - AUC metric at a specific time point, with lower values indicating better performance.
-#' @section References:
#' @section References:
See also
diff --git a/reference/loss_one_minus_integrated_cd_auc.html b/reference/loss_one_minus_integrated_cd_auc.html index 28065860..bb270f44 100644 --- a/reference/loss_one_minus_integrated_cd_auc.html +++ b/reference/loss_one_minus_integrated_cd_auc.html @@ -25,7 +25,7 @@Value
numeric from 0 to 1, lower values indicate better performance
-#' @section References:
#' @section References:
See also
diff --git a/reference/model_diagnostics.surv_explainer-1.png b/reference/model_diagnostics.surv_explainer-1.png index 395f6418..8442ba8c 100644 Binary files a/reference/model_diagnostics.surv_explainer-1.png and b/reference/model_diagnostics.surv_explainer-1.png differ diff --git a/reference/model_diagnostics.surv_explainer-2.png b/reference/model_diagnostics.surv_explainer-2.png index 6fb06777..3f394b05 100644 Binary files a/reference/model_diagnostics.surv_explainer-2.png and b/reference/model_diagnostics.surv_explainer-2.png differ diff --git a/reference/model_diagnostics.surv_explainer.html b/reference/model_diagnostics.surv_explainer.html index ab8ff665..a038a766 100644 --- a/reference/model_diagnostics.surv_explainer.html +++ b/reference/model_diagnostics.surv_explainer.html @@ -25,7 +25,7 @@Examples
cph_model_parts_brier <- model_parts(cph_exp) print(head(cph_model_parts_brier$result)) #> _times_ _full_model_ trt celltype karno diagtime -#> 1 1.5 0 5.711386e-05 -6.096888e-04 0.0001393343 3.821256e-07 -#> 12 4.0 0 -2.062020e-04 -7.710922e-04 0.0016566157 1.949591e-06 -#> 23 7.0 0 -8.488481e-05 3.500556e-05 0.0047336098 2.447634e-06 -#> 35 8.0 0 -7.363275e-04 1.916431e-03 0.0092822467 7.872084e-06 -#> 48 10.0 0 -1.657157e-03 5.112928e-04 0.0124710562 9.628609e-06 -#> 57 12.0 0 -2.972751e-03 1.719285e-04 0.0142232539 9.751368e-06 -#> age prior _baseline_ _permutation_ label -#> 1 3.338393e-05 6.958098e-06 6.475838e-05 0 coxph -#> 12 5.516239e-04 1.527996e-05 2.198293e-03 0 coxph -#> 23 7.071693e-04 -7.498747e-05 6.950997e-03 0 coxph -#> 35 8.005002e-04 1.231513e-04 1.268998e-02 0 coxph -#> 48 9.317906e-04 2.202477e-04 1.547096e-02 0 coxph -#> 57 1.041125e-03 4.832997e-04 1.658093e-02 0 coxph +#> 1 1.5 0 5.256927e-05 -0.0004332791 0.0002107716 9.425258e-08 +#> 12 4.0 0 -7.430163e-05 -0.0002120110 0.0018899828 1.491936e-06 +#> 23 7.0 0 2.436943e-04 0.0009262378 0.0055721212 2.720173e-06 +#> 35 8.0 0 6.340322e-05 0.0030181084 0.0110558455 5.798739e-06 +#> 48 10.0 0 -6.445876e-04 0.0011089194 0.0144497411 7.101680e-06 +#> 57 12.0 0 -1.618452e-03 0.0010867304 0.0182603948 7.471981e-06 +#> age prior _baseline_ _permutation_ label +#> 1 1.865661e-05 1.246491e-05 -1.297031e-05 0 coxph +#> 12 5.460953e-04 1.124311e-05 2.361437e-03 0 coxph +#> 23 7.186469e-04 -7.165984e-05 6.461760e-03 0 coxph +#> 35 8.083450e-04 9.299490e-05 1.206747e-02 0 coxph +#> 48 1.076200e-03 2.029838e-04 1.578048e-02 0 coxph +#> 57 1.107444e-03 4.655921e-04 1.838133e-02 0 coxph plot(cph_model_parts_brier)
@@ -213,19 +213,19 @@ Examples
rsf_ranger_model_parts <- model_parts(rsf_ranger_exp) print(head(rsf_ranger_model_parts$result)) #> _times_ _full_model_ trt celltype karno diagtime -#> 1 1.5 0 0.0003793868 0.002454174 0.002851872 0.001262180 -#> 12 4.0 0 0.0011696720 0.003298816 0.010505938 0.002867416 -#> 23 7.0 0 0.0020057892 0.003163530 0.018409967 0.004878307 -#> 35 8.0 0 0.0021550761 0.005071603 0.026164134 0.009821355 -#> 48 10.0 0 0.0022776079 0.005415863 0.038269444 0.011817754 -#> 57 12.0 0 0.0025514858 0.007117975 0.043247451 0.013755656 -#> age prior _baseline_ _permutation_ label -#> 1 0.002470737 0.0003628108 0.00570029 0 ranger -#> 12 0.010552052 0.0005592063 0.01842194 0 ranger -#> 23 0.013647414 0.0007304643 0.02606660 0 ranger -#> 35 0.013705104 0.0014291307 0.03898082 0 ranger -#> 48 0.014564055 0.0017069587 0.04940442 0 ranger -#> 57 0.015033881 0.0029664509 0.05557629 0 ranger +#> 1 1.5 0 0.0002258422 0.001653738 0.002154695 0.001362203 +#> 12 4.0 0 0.0011207249 0.002459063 0.009308037 0.003410368 +#> 23 7.0 0 0.0014091835 0.003266176 0.016353161 0.004723243 +#> 35 8.0 0 0.0016703949 0.005425894 0.025991293 0.010953146 +#> 48 10.0 0 0.0020650091 0.005600829 0.036227678 0.013873516 +#> 57 12.0 0 0.0026297856 0.007873114 0.042640749 0.015943279 +#> age prior _baseline_ _permutation_ label +#> 1 0.00231664 0.0002449811 0.005470502 0 ranger +#> 12 0.01019244 0.0006931186 0.019393118 0 ranger +#> 23 0.01330631 0.0011607115 0.027884967 0 ranger +#> 35 0.01388119 0.0019286487 0.042736373 0 ranger +#> 48 0.01540402 0.0032001327 0.057502499 0 ranger +#> 57 0.01629742 0.0046273271 0.063597659 0 ranger plot(cph_model_parts_brier, rsf_ranger_model_parts)
# }
diff --git a/reference/model_performance.surv_explainer-1.png b/reference/model_performance.surv_explainer-1.png
index 5509dc92..5c98e35b 100644
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diff --git a/reference/model_performance.surv_explainer-2.png b/reference/model_performance.surv_explainer-2.png
index 4aeba722..2b8a047f 100644
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diff --git a/reference/model_performance.surv_explainer.html b/reference/model_performance.surv_explainer.html
index 4c766c2a..cc466628 100644
--- a/reference/model_performance.surv_explainer.html
+++ b/reference/model_performance.surv_explainer.html
@@ -25,7 +25,7 @@
@@ -123,9 +123,9 @@ Arguments
Value
-An object of class "model_performance_survival". It's a list of metric values calculated for the model. It contains:
An object of class "model_performance_survival". It's a list of metric values calculated for the model. It contains:
Value
References
-Examples
head(cph_model_profile$result) #> _vname_ _vtype_ _label_ _x_ _times_ _yhat_ _ids_ -#> 1 age numerical coxph 34.00 1.5 0.9824841 0 -#> 2 age numerical coxph 34.94 1.5 0.9826247 0 -#> 3 age numerical coxph 35.00 1.5 0.9826336 0 -#> 4 age numerical coxph 35.88 1.5 0.9827642 0 -#> 5 age numerical coxph 36.00 1.5 0.9827819 0 -#> 6 age numerical coxph 36.82 1.5 0.9829026 0 +#> 1 age numerical coxph 34.00 1.5 0.9811826 0 +#> 2 age numerical coxph 34.94 1.5 0.9813333 0 +#> 3 age numerical coxph 35.00 1.5 0.9813428 0 +#> 4 age numerical coxph 35.88 1.5 0.9814827 0 +#> 5 age numerical coxph 36.00 1.5 0.9815017 0 +#> 6 age numerical coxph 36.82 1.5 0.9816310 0 plot(cph_model_profile)
@@ -230,12 +230,12 @@ Examples
head(rsf_model_profile$result) #> _vname_ _vtype_ _label_ _x_ _times_ _yhat_ _ids_ -#> 1 age numerical rfsrc 35 1.5 0.9794243 0 -#> 2 age numerical rfsrc 35 4.0 0.9516358 0 -#> 3 age numerical rfsrc 35 7.0 0.9335097 0 -#> 4 age numerical rfsrc 35 8.0 0.9082394 0 -#> 5 age numerical rfsrc 35 10.0 0.8938437 0 -#> 6 age numerical rfsrc 35 12.0 0.8728426 0 +#> 1 age numerical rfsrc 34 1.5 0.8168101 0 +#> 2 age numerical rfsrc 34 4.0 0.7394619 0 +#> 3 age numerical rfsrc 34 7.0 0.7221792 0 +#> 4 age numerical rfsrc 34 8.0 0.6995498 0 +#> 5 age numerical rfsrc 34 10.0 0.6866869 0 +#> 6 age numerical rfsrc 34 12.0 0.6744986 0 plot(rsf_model_profile, variables = c("age", "celltype"), numerical_plot_type = "contours")
diff --git a/reference/model_profile_2d.surv_explainer-1.png b/reference/model_profile_2d.surv_explainer-1.png
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diff --git a/reference/model_profile_2d.surv_explainer-2.png b/reference/model_profile_2d.surv_explainer-2.png
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diff --git a/reference/model_profile_2d.surv_explainer.html b/reference/model_profile_2d.surv_explainer.html
index 718ac3cb..7bd19764 100644
--- a/reference/model_profile_2d.surv_explainer.html
+++ b/reference/model_profile_2d.surv_explainer.html
@@ -26,7 +26,7 @@
@@ -188,12 +188,12 @@ Examples
#> 5 age celltype numerical categorical 41.8333333333333 adeno 1.5 #> 6 age celltype numerical categorical 43.7916666666667 adeno 1.5 #> _label_ _yhat_ -#> 1 coxph 0.9744180 -#> 2 coxph 0.9748422 -#> 3 coxph 0.9752594 -#> 4 coxph 0.9756699 -#> 5 coxph 0.9760737 -#> 6 coxph 0.9764709 +#> 1 coxph 0.9728298 +#> 2 coxph 0.9732800 +#> 3 coxph 0.9737229 +#> 4 coxph 0.9741587 +#> 5 coxph 0.9745873 +#> 6 coxph 0.9750089 plot(cph_model_profile_2d) #> Warning: Plot will be prepared for the median survial time. For another time point, set the value of `times`.
@@ -204,19 +204,19 @@ Examples
) head(cph_model_profile_2d_ale$result) #> _v1name_ _v2name_ _v1type_ _v2type_ _v1value_ _v2value_ _times_ _yhat_ -#> 1 age karno numerical numerical 35 10 1.5 0.9803683 -#> 2 age karno numerical numerical 35 10 4.0 0.9519167 -#> 3 age karno numerical numerical 35 10 7.0 0.9242215 -#> 4 age karno numerical numerical 35 10 8.0 0.8885757 -#> 5 age karno numerical numerical 35 10 10.0 0.8714207 -#> 6 age karno numerical numerical 35 10 12.0 0.8467737 +#> 1 age karno numerical numerical 34 10 1.5 0.9826099 +#> 2 age karno numerical numerical 34 10 4.0 0.9575688 +#> 3 age karno numerical numerical 34 10 7.0 0.9333803 +#> 4 age karno numerical numerical 34 10 8.0 0.9025179 +#> 5 age karno numerical numerical 34 10 10.0 0.8877701 +#> 6 age karno numerical numerical 34 10 12.0 0.8666934 #> _right_ _left_ _top_ _bottom_ _count_ _label_ -#> 1 36.5 35 10 15 0 coxph -#> 2 36.5 35 10 15 0 coxph -#> 3 36.5 35 10 15 0 coxph -#> 4 36.5 35 10 15 0 coxph -#> 5 36.5 35 10 15 0 coxph -#> 6 36.5 35 10 15 0 coxph +#> 1 36 34 10 20 0 coxph +#> 2 36 34 10 20 0 coxph +#> 3 36 34 10 20 0 coxph +#> 4 36 34 10 20 0 coxph +#> 5 36 34 10 20 0 coxph +#> 6 36 34 10 20 0 coxph plot(cph_model_profile_2d_ale) #> Warning: Plot will be prepared for the median survial time. For another time point, set the value of `times`.
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index f2be0868..e8fa5a80 100644
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+++ b/reference/model_survshap.surv_explainer.html
@@ -25,7 +25,7 @@
@@ -90,6 +90,7 @@ Global SHAP Values
explainer, new_observation = NULL, y_true = NULL, + N = NULL, calculation_method = "kernelshap", aggregation_method = "integral", output_type = "survival", @@ -115,8 +116,12 @@Arguments
plot.aggregated_surv_shap
#' ## plot.aggregated_surv_shap(geom = "curves")
+
+
+plot.aggregated_surv_shap(geom = "curves")
+
+
@@ -209,8 +215,11 @@ Examples
#> Observations with outlying SurvSHAP(t) values:
#> trt celltype karno diagtime age prior
#> 1 1 squamous 60 7 69 0
+#> 2 1 squamous 70 5 64 10
#> 3 1 squamous 60 3 38 0
+#> 4 1 squamous 60 9 63 10
#> 18 1 smallcell 40 2 35 0
+#> 19 1 smallcell 80 4 63 10
#> 127 2 large 70 15 68 10
#> 128 2 large 30 4 39 10
diff --git a/reference/plot.model_diagnostics_survival.html b/reference/plot.model_diagnostics_survival.html
index 5f6bd64b..e206a2c6 100644
--- a/reference/plot.model_diagnostics_survival.html
+++ b/reference/plot.model_diagnostics_survival.html
@@ -26,7 +26,7 @@
diff --git a/reference/plot.model_parts_survival-1.png b/reference/plot.model_parts_survival-1.png
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--- a/reference/plot.model_parts_survival.html
+++ b/reference/plot.model_parts_survival.html
@@ -26,7 +26,7 @@
diff --git a/reference/plot.model_performance_survival-1.png b/reference/plot.model_performance_survival-1.png
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index 394e7629..960f2f88 100644
--- a/reference/plot.model_performance_survival.html
+++ b/reference/plot.model_performance_survival.html
@@ -26,7 +26,7 @@
diff --git a/reference/plot.model_profile_2d_survival-1.png b/reference/plot.model_profile_2d_survival-1.png
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index e79b0b52..0d29c4e1 100644
--- a/reference/plot.model_profile_2d_survival.html
+++ b/reference/plot.model_profile_2d_survival.html
@@ -26,7 +26,7 @@
@@ -172,19 +172,19 @@ Examples
)
head(cph_model_profile_2d$result)
#> _v1name_ _v2name_ _v1type_ _v2type_ _v1value_ _v2value_ _times_
-#> 1 age celltype numerical categorical 34 adeno 1.5
-#> 2 age celltype numerical categorical 35.9583333333333 adeno 1.5
-#> 3 age celltype numerical categorical 37.9166666666667 adeno 1.5
-#> 4 age celltype numerical categorical 39.875 adeno 1.5
-#> 5 age celltype numerical categorical 41.8333333333333 adeno 1.5
-#> 6 age celltype numerical categorical 43.7916666666667 adeno 1.5
+#> 1 age celltype numerical categorical 35 adeno 1.5
+#> 2 age celltype numerical categorical 36.5416666666667 adeno 1.5
+#> 3 age celltype numerical categorical 38.0833333333333 adeno 1.5
+#> 4 age celltype numerical categorical 39.625 adeno 1.5
+#> 5 age celltype numerical categorical 41.1666666666667 adeno 1.5
+#> 6 age celltype numerical categorical 42.7083333333333 adeno 1.5
#> _label_ _yhat_
-#> 1 coxph 0.9721413
-#> 2 coxph 0.9726016
-#> 3 coxph 0.9730544
-#> 4 coxph 0.9734999
-#> 5 coxph 0.9739382
-#> 6 coxph 0.9743694
+#> 1 coxph 0.9698524
+#> 2 coxph 0.9702453
+#> 3 coxph 0.9706333
+#> 4 coxph 0.9710162
+#> 5 coxph 0.9713943
+#> 6 coxph 0.9717676
plot(cph_model_profile_2d, variables = list(c("age", "celltype")), times = cph_exp$times[20])
@@ -194,19 +194,19 @@ Examples
)
head(cph_model_profile_2d_ale$result)
#> _v1name_ _v2name_ _v1type_ _v2type_ _v1value_ _v2value_ _times_ _yhat_
-#> 1 age karno numerical numerical 34 10 1.5 0.9808874
-#> 2 age karno numerical numerical 34 10 4.0 0.9534604
-#> 3 age karno numerical numerical 34 10 7.0 0.9270583
-#> 4 age karno numerical numerical 34 10 8.0 0.8934681
-#> 5 age karno numerical numerical 34 10 10.0 0.8774408
-#> 6 age karno numerical numerical 34 10 12.0 0.8545444
+#> 1 age karno numerical numerical 34 10 1.5 0.9808187
+#> 2 age karno numerical numerical 34 10 4.0 0.9529722
+#> 3 age karno numerical numerical 34 10 7.0 0.9258121
+#> 4 age karno numerical numerical 34 10 8.0 0.8907776
+#> 5 age karno numerical numerical 34 10 10.0 0.8738869
+#> 6 age karno numerical numerical 34 10 12.0 0.8495871
#> _right_ _left_ _top_ _bottom_ _count_ _label_
-#> 1 36 34 10 20 0 coxph
-#> 2 36 34 10 20 0 coxph
-#> 3 36 34 10 20 0 coxph
-#> 4 36 34 10 20 0 coxph
-#> 5 36 34 10 20 0 coxph
-#> 6 36 34 10 20 0 coxph
+#> 1 36 34 10 15 0 coxph
+#> 2 36 34 10 15 0 coxph
+#> 3 36 34 10 15 0 coxph
+#> 4 36 34 10 15 0 coxph
+#> 5 36 34 10 15 0 coxph
+#> 6 36 34 10 15 0 coxph
plot(cph_model_profile_2d_ale, times = cph_exp$times[c(10, 20)], marginalize_over_time = TRUE)
# }
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+++ b/reference/plot.model_profile_survival.html
@@ -26,7 +26,7 @@
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index 860ca9b9..518eeb28 100644
--- a/reference/plot.predict_parts_survival.html
+++ b/reference/plot.predict_parts_survival.html
@@ -26,7 +26,7 @@
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index 930b986c..1b4963e0 100644
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+++ b/reference/plot.predict_profile_survival.html
@@ -26,7 +26,7 @@
diff --git a/reference/plot.surv_feature_importance-1.png b/reference/plot.surv_feature_importance-1.png
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index 1fe7f405..2d521a1d 100644
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+++ b/reference/plot.surv_feature_importance.html
@@ -27,7 +27,7 @@
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index d657f812..f1b07ef1 100644
--- a/reference/plot.surv_lime.html
+++ b/reference/plot.surv_lime.html
@@ -26,7 +26,7 @@
diff --git a/reference/plot.surv_model_performance-1.png b/reference/plot.surv_model_performance-1.png
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index 355eabf4..d1972bad 100644
--- a/reference/plot.surv_model_performance.html
+++ b/reference/plot.surv_model_performance.html
@@ -26,7 +26,7 @@
diff --git a/reference/plot.surv_model_performance_rocs-1.png b/reference/plot.surv_model_performance_rocs-1.png
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index 5e294edb..e1220d2f 100644
--- a/reference/plot.surv_model_performance_rocs.html
+++ b/reference/plot.surv_model_performance_rocs.html
@@ -26,7 +26,7 @@
diff --git a/reference/plot.surv_shap-1.png b/reference/plot.surv_shap-1.png
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index e13082c3..2de0711d 100644
--- a/reference/plot.surv_shap.html
+++ b/reference/plot.surv_shap.html
@@ -26,7 +26,7 @@
diff --git a/reference/predict.surv_explainer.html b/reference/predict.surv_explainer.html
index 34b7e499..42254687 100644
--- a/reference/predict.surv_explainer.html
+++ b/reference/predict.surv_explainer.html
@@ -25,7 +25,7 @@
@@ -170,8 +170,8 @@ Examples
#> 0.7354128
predict(rsf_ranger_exp, veteran[1, ], output_type = "chf")[, 1:10]
-#> [1] 0.005709320 0.005931542 0.011686444 0.012941830 0.012941830 0.027183645
-#> [7] 0.031544223 0.035825326 0.039158659 0.039158659
+#> [1] 0.009623403 0.009623403 0.027945423 0.033625319 0.035069764 0.061802719
+#> [7] 0.067580103 0.073109355 0.073219245 0.075695435
diff --git a/reference/predict_parts.surv_explainer-2.png b/reference/predict_parts.surv_explainer-2.png
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index bb097276..b598aa4b 100644
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+++ b/reference/predict_parts.surv_explainer.html
@@ -25,7 +25,7 @@
@@ -112,7 +112,7 @@ Arguments
Arguments
Additional parameters
#' ## plot.aggregated_surv_shap(geom = "curves")
plot.aggregated_surv_shap(geom = "curves")
+
+Examples
#> Observations with outlying SurvSHAP(t) values: #> trt celltype karno diagtime age prior #> 1 1 squamous 60 7 69 0 +#> 2 1 squamous 70 5 64 10 #> 3 1 squamous 60 3 38 0 +#> 4 1 squamous 60 9 63 10 #> 18 1 smallcell 40 2 35 0 +#> 19 1 smallcell 80 4 63 10 #> 127 2 large 70 15 68 10 #> 128 2 large 30 4 39 10
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index 5f6bd64b..e206a2c6 100644
--- a/reference/plot.model_diagnostics_survival.html
+++ b/reference/plot.model_diagnostics_survival.html
@@ -26,7 +26,7 @@
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+++ b/reference/plot.model_parts_survival.html
@@ -26,7 +26,7 @@
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--- a/reference/plot.model_performance_survival.html
+++ b/reference/plot.model_performance_survival.html
@@ -26,7 +26,7 @@
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--- a/reference/plot.model_profile_2d_survival.html
+++ b/reference/plot.model_profile_2d_survival.html
@@ -26,7 +26,7 @@
@@ -172,19 +172,19 @@ Examples
) head(cph_model_profile_2d$result) #> _v1name_ _v2name_ _v1type_ _v2type_ _v1value_ _v2value_ _times_ -#> 1 age celltype numerical categorical 34 adeno 1.5 -#> 2 age celltype numerical categorical 35.9583333333333 adeno 1.5 -#> 3 age celltype numerical categorical 37.9166666666667 adeno 1.5 -#> 4 age celltype numerical categorical 39.875 adeno 1.5 -#> 5 age celltype numerical categorical 41.8333333333333 adeno 1.5 -#> 6 age celltype numerical categorical 43.7916666666667 adeno 1.5 +#> 1 age celltype numerical categorical 35 adeno 1.5 +#> 2 age celltype numerical categorical 36.5416666666667 adeno 1.5 +#> 3 age celltype numerical categorical 38.0833333333333 adeno 1.5 +#> 4 age celltype numerical categorical 39.625 adeno 1.5 +#> 5 age celltype numerical categorical 41.1666666666667 adeno 1.5 +#> 6 age celltype numerical categorical 42.7083333333333 adeno 1.5 #> _label_ _yhat_ -#> 1 coxph 0.9721413 -#> 2 coxph 0.9726016 -#> 3 coxph 0.9730544 -#> 4 coxph 0.9734999 -#> 5 coxph 0.9739382 -#> 6 coxph 0.9743694 +#> 1 coxph 0.9698524 +#> 2 coxph 0.9702453 +#> 3 coxph 0.9706333 +#> 4 coxph 0.9710162 +#> 5 coxph 0.9713943 +#> 6 coxph 0.9717676 plot(cph_model_profile_2d, variables = list(c("age", "celltype")), times = cph_exp$times[20])
@@ -194,19 +194,19 @@ Examples
) head(cph_model_profile_2d_ale$result) #> _v1name_ _v2name_ _v1type_ _v2type_ _v1value_ _v2value_ _times_ _yhat_ -#> 1 age karno numerical numerical 34 10 1.5 0.9808874 -#> 2 age karno numerical numerical 34 10 4.0 0.9534604 -#> 3 age karno numerical numerical 34 10 7.0 0.9270583 -#> 4 age karno numerical numerical 34 10 8.0 0.8934681 -#> 5 age karno numerical numerical 34 10 10.0 0.8774408 -#> 6 age karno numerical numerical 34 10 12.0 0.8545444 +#> 1 age karno numerical numerical 34 10 1.5 0.9808187 +#> 2 age karno numerical numerical 34 10 4.0 0.9529722 +#> 3 age karno numerical numerical 34 10 7.0 0.9258121 +#> 4 age karno numerical numerical 34 10 8.0 0.8907776 +#> 5 age karno numerical numerical 34 10 10.0 0.8738869 +#> 6 age karno numerical numerical 34 10 12.0 0.8495871 #> _right_ _left_ _top_ _bottom_ _count_ _label_ -#> 1 36 34 10 20 0 coxph -#> 2 36 34 10 20 0 coxph -#> 3 36 34 10 20 0 coxph -#> 4 36 34 10 20 0 coxph -#> 5 36 34 10 20 0 coxph -#> 6 36 34 10 20 0 coxph +#> 1 36 34 10 15 0 coxph +#> 2 36 34 10 15 0 coxph +#> 3 36 34 10 15 0 coxph +#> 4 36 34 10 15 0 coxph +#> 5 36 34 10 15 0 coxph +#> 6 36 34 10 15 0 coxph plot(cph_model_profile_2d_ale, times = cph_exp$times[c(10, 20)], marginalize_over_time = TRUE)
# }
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--- a/reference/plot.surv_model_performance_rocs.html
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@@ -26,7 +26,7 @@
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