Engine docs for censored

man/rmd/C5_rules_C5.0.md

@@ -7,10 +7,12 @@ For this engine, there is a single mode: classification
 
 
 
-This model has 1 tuning parameters:
+This model has 2 tuning parameters:
 
 - `trees`: # Trees (type: integer, default: 1L)
 
+- `min_n`: Minimal Node Size (type: integer, default: 2L)
+
 Note that C5.0 has a tool for _early stopping_ during boosting where less iterations of boosting are performed than the number requested. `C5_rules()` turns this feature off (although it can be re-enabled using [C50::C5.0Control()]).
 
 ## Translation from parsnip to the underlying model call  (classification)

man/rmd/bag_tree_rpart.Rmd

@@ -77,6 +77,11 @@ bag_tree(tree_depth = integer(1), min_n = integer(1), cost_complexity = double(1
 ```{r child = "template-uses-case-weights.Rmd"}
 ```
 
+## Other details
+
+```{r child = "template-survival-median.Rmd"}
+```
+
 ## References
 
  - Breiman L. 1996. "Bagging predictors". Machine Learning. 24 (2): 123-140

man/rmd/bag_tree_rpart.md

@@ -107,7 +107,8 @@ bag_tree(tree_depth = integer(1), min_n = integer(1), cost_complexity = double(1
 ## 
 ## Model fit template:
 ## ipred::bagging(formula = missing_arg(), data = missing_arg(), 
-##     cp = double(1), maxdepth = integer(1), minsplit = integer(1))
+##     weights = missing_arg(), cp = double(1), maxdepth = integer(1), 
+##     minsplit = integer(1))
 ```
 
 
@@ -123,6 +124,12 @@ This model can utilize case weights during model fitting. To use them, see the d
 
 The `fit()` and `fit_xy()` arguments have arguments called `case_weights` that expect vectors of case weights. 
 
+## Other details
+
+
+
+Predictions of type `"time"` are predictions of the median survival time.
+
 ## References
 
  - Breiman L. 1996. "Bagging predictors". Machine Learning. 24 (2): 123-140

man/rmd/boost_tree_mboost.Rmd

@@ -45,6 +45,11 @@ boost_tree() %>%
 ```{r child = "template-tree-split-factors.Rmd"}
 ```
 
+## Other details
+
+```{r child = "template-survival-mean.Rmd"}
+```
+
 ## References
 
  - Buehlmann P, Hothorn T. 2007. Boosting algorithms: regularization, prediction and model fitting. _Statistical Science_, 22(4), 477–505.

man/rmd/boost_tree_mboost.md

@@ -42,7 +42,7 @@ boost_tree() %>%
 ## 
 ## Model fit template:
 ## censored::blackboost_train(formula = missing_arg(), data = missing_arg(), 
-##     family = mboost::CoxPH())
+##     weights = missing_arg(), family = mboost::CoxPH())
 ```
 
 `censored::blackboost_train()` is a wrapper around [mboost::blackboost()] (and other functions) that makes it easier to run this model. 
@@ -52,6 +52,12 @@ boost_tree() %>%
 
 This engine does not require any special encoding of the predictors. Categorical predictors can be partitioned into groups of factor levels (e.g. `{a, c}` vs `{b, d}`) when splitting at a node. Dummy variables are not required for this model. 
 
+## Other details
+
+
+
+Predictions of type `"time"` are predictions of the mean survival time.
+
 ## References
 
  - Buehlmann P, Hothorn T. 2007. Boosting algorithms: regularization, prediction and model fitting. _Statistical Science_, 22(4), 477–505.

man/rmd/decision_tree_partykit.Rmd

@@ -78,6 +78,11 @@ decision_tree(tree_depth = integer(1), min_n = integer(1)) %>%
 ```{r child = "template-tree-split-factors.Rmd"}
 ```
 
+## Other details
+
+```{r child = "template-survival-median.Rmd"}
+```
+
 ## References
 
  - [partykit: A Modular Toolkit for Recursive Partytioning in R](https://jmlr.org/papers/v16/hothorn15a.html)

man/rmd/decision_tree_partykit.md

@@ -104,7 +104,8 @@ decision_tree(tree_depth = integer(1), min_n = integer(1)) %>%
 ## 
 ## Model fit template:
 ## parsnip::ctree_train(formula = missing_arg(), data = missing_arg(), 
-##     maxdepth = integer(1), minsplit = min_rows(0L, data))
+##     weights = missing_arg(), maxdepth = integer(1), minsplit = min_rows(0L, 
+##         data))
 ```
 
 `censored::cond_inference_surv_ctree()` is a wrapper around [partykit::ctree()] (and other functions) that makes it easier to run this model. 
@@ -114,6 +115,12 @@ decision_tree(tree_depth = integer(1), min_n = integer(1)) %>%
 
 This engine does not require any special encoding of the predictors. Categorical predictors can be partitioned into groups of factor levels (e.g. `{a, c}` vs `{b, d}`) when splitting at a node. Dummy variables are not required for this model. 
 
+## Other details
+
+
+
+Predictions of type `"time"` are predictions of the median survival time.
+
 ## References
 
  - [partykit: A Modular Toolkit for Recursive Partytioning in R](https://jmlr.org/papers/v16/hothorn15a.html)

man/rmd/decision_tree_rpart.Rmd

@@ -70,6 +70,11 @@ decision_tree(
 ```{r child = "template-uses-case-weights.Rmd"}
 ```
 
+## Other details
+
+```{r child = "template-survival-mean.Rmd"}
+```
+
 ## Examples 
 
 The "Fitting and Predicting with parsnip" article contains [examples](https://parsnip.tidymodels.org/articles/articles/Examples.html#decision-tree-rpart) for `decision_tree()` with the `"rpart"` engine.

man/rmd/decision_tree_rpart.md

@@ -99,8 +99,8 @@ decision_tree(
 ## 
 ## Model fit template:
 ## pec::pecRpart(formula = missing_arg(), data = missing_arg(), 
-##     cp = double(1), maxdepth = integer(1), minsplit = min_rows(0L, 
-##         data))
+##     weights = missing_arg(), cp = double(1), maxdepth = integer(1), 
+##     minsplit = min_rows(0L, data))
 ```
 
 ## Preprocessing requirements
@@ -115,6 +115,12 @@ This model can utilize case weights during model fitting. To use them, see the d
 
 The `fit()` and `fit_xy()` arguments have arguments called `case_weights` that expect vectors of case weights. 
 
+## Other details
+
+
+
+Predictions of type `"time"` are predictions of the mean survival time.
+
 ## Examples 
 
 The "Fitting and Predicting with parsnip" article contains [examples](https://parsnip.tidymodels.org/articles/articles/Examples.html#decision-tree-rpart) for `decision_tree()` with the `"rpart"` engine.

man/rmd/discrim_flexible_earth.md

@@ -44,8 +44,9 @@ discrim_flexible(
 ## Computational engine: earth 
 ## 
 ## Model fit template:
-## mda::fda(formula = missing_arg(), data = missing_arg(), nprune = integer(0), 
-##     degree = integer(0), pmethod = character(0), method = earth::earth)
+## mda::fda(formula = missing_arg(), data = missing_arg(), weights = missing_arg(), 
+##     nprune = integer(0), degree = integer(0), pmethod = character(0), 
+##     method = earth::earth)
 ```
 
 ## Preprocessing requirements

man/rmd/discrim_linear_mda.md

@@ -34,8 +34,8 @@ discrim_linear(penalty = numeric(0)) %>%
 ## Computational engine: mda 
 ## 
 ## Model fit template:
-## mda::fda(formula = missing_arg(), data = missing_arg(), lambda = numeric(0), 
-##     method = mda::gen.ridge, keep.fitted = FALSE)
+## mda::fda(formula = missing_arg(), data = missing_arg(), weights = missing_arg(), 
+##     lambda = numeric(0), method = mda::gen.ridge, keep.fitted = FALSE)
 ```
 
 ## Preprocessing requirements

man/rmd/gen_additive_mod_mgcv.md

@@ -26,7 +26,7 @@ gen_additive_mod(adjust_deg_free = numeric(1), select_features = logical(1)) %>%
 ```
 
 ```
-## GAM Specification (regression)
+## GAM Model Specification (regression)
 ## 
 ## Main Arguments:
 ##   select_features = logical(1)
@@ -50,7 +50,7 @@ gen_additive_mod(adjust_deg_free = numeric(1), select_features = logical(1)) %>%
 ```
 
 ```
-## GAM Specification (classification)
+## GAM Model Specification (classification)
 ## 
 ## Main Arguments:
 ##   select_features = logical(1)

man/rmd/mlp_brulee.md

@@ -53,7 +53,7 @@ mlp(
 ```
 
 ```
-## Single Layer Neural Network Specification (regression)
+## Single Layer Neural Network Model Specification (regression)
 ## 
 ## Main Arguments:
 ##   hidden_units = integer(1)
@@ -91,7 +91,7 @@ mlp(
 ```
 
 ```
-## Single Layer Neural Network Specification (classification)
+## Single Layer Neural Network Model Specification (classification)
 ## 
 ## Main Arguments:
 ##   hidden_units = integer(1)