Merge pull request #651 from tidymodels/doc-template-linear-pred

use template for extra info on linear predictor

Author: hfrick

man/rmd/proportional_hazards_glmnet.Rmd

@@ -85,14 +85,8 @@ predict(mod, pred_data, type = "survival", time = 500) %>%
 Note that columns used in the `strata()` function _will_ also be estimated in the regular portion of the model (i.e., within the linear predictor).
 
 # Linear predictor values
-
-Since risk regression and parametric survival models are modeling different characteristics (e.g. relative hazard versus event time), their linear predictors will be going in opposite directions. 
-
-For example, for parametric models, the linear predictor _increases with time_. For proportional hazards models the linear predictor _decreases with time_ (since hazard is increasing). As such, the linear predictors for these two quantities will have opposite signs.
-
-tidymodels does not treat different models differently when computing performance metrics.  To standardize across model types, the default for proportional hazards models is to have _increasing values with time_. As a result, the sign of the linear predictor will be the opposite of the value produced by the `predict()` method in the \pkg{survival} package. 
-
-This behavior can be changed by using the `increasing` argument when calling `predict()` on a \pkg{parsnip} model object. 
+```{r child = "template-censored-linear-predictor.Rmd"}
+```
 
 # References
 

man/rmd/proportional_hazards_glmnet.md

@@ -101,7 +101,7 @@ Since risk regression and parametric survival models are modeling different char
 
 For example, for parametric models, the linear predictor _increases with time_. For proportional hazards models the linear predictor _decreases with time_ (since hazard is increasing). As such, the linear predictors for these two quantities will have opposite signs.
 
-tidymodels does not treat different models differently when computing performance metrics.  To standardize across model types, the default for proportional hazards models is to have _increasing values with time_. As a result, the sign of the linear predictor will be the opposite of the value produced by the `predict()` method in the \pkg{survival} package. 
+tidymodels does not treat different models differently when computing performance metrics.  To standardize across model types, the default for proportional hazards models is to have _increasing values with time_. As a result, the sign of the linear predictor will be the opposite of the value produced by the `predict()` method in the engine package. 
 
 This behavior can be changed by using the `increasing` argument when calling `predict()` on a \pkg{parsnip} model object. 
 

man/rmd/proportional_hazards_survival.Rmd

@@ -44,14 +44,8 @@ Note that columns used in the `strata()` function will not be estimated in the r
 
 
 # Linear predictor values
-
-Since risk regression and parametric survival models are modeling different characteristics (e.g. relative hazard versus event time), their linear predictors will be going in opposite directions. 
-
-For example, for parametric models, the linear predictor _increases with time_. For proportional hazards models the linear predictor _decreases with time_ (since hazard is increasing). As such, the linear predictors for these two quantities will have opposite signs.
-
-tidymodels does not treat different models differently when computing performance metrics.  To standardize across model types, the default for proportional hazards models is to have _increasing values with time_. As a result, the sign of the linear predictor will be the opposite of the value produced by the `predict()` method in the \pkg{survival} package. 
-
-This behavior can be changed by using the `increasing` argument when calling `predict()` on a \pkg{parsnip} model object. 
+```{r child = "template-censored-linear-predictor.Rmd"}
+```
 
 ## References
 

man/rmd/proportional_hazards_survival.md

@@ -91,7 +91,7 @@ Since risk regression and parametric survival models are modeling different char
 
 For example, for parametric models, the linear predictor _increases with time_. For proportional hazards models the linear predictor _decreases with time_ (since hazard is increasing). As such, the linear predictors for these two quantities will have opposite signs.
 
-tidymodels does not treat different models differently when computing performance metrics.  To standardize across model types, the default for proportional hazards models is to have _increasing values with time_. As a result, the sign of the linear predictor will be the opposite of the value produced by the `predict()` method in the \pkg{survival} package. 
+tidymodels does not treat different models differently when computing performance metrics.  To standardize across model types, the default for proportional hazards models is to have _increasing values with time_. As a result, the sign of the linear predictor will be the opposite of the value produced by the `predict()` method in the engine package. 
 
 This behavior can be changed by using the `increasing` argument when calling `predict()` on a \pkg{parsnip} model object. 
 

man/rmd/template-censored-linear-predictor.Rmd

@@ -0,0 +1,7 @@
+Since risk regression and parametric survival models are modeling different characteristics (e.g. relative hazard versus event time), their linear predictors will be going in opposite directions. 
+
+For example, for parametric models, the linear predictor _increases with time_. For proportional hazards models the linear predictor _decreases with time_ (since hazard is increasing). As such, the linear predictors for these two quantities will have opposite signs.
+
+tidymodels does not treat different models differently when computing performance metrics.  To standardize across model types, the default for proportional hazards models is to have _increasing values with time_. As a result, the sign of the linear predictor will be the opposite of the value produced by the `predict()` method in the engine package. 
+
+This behavior can be changed by using the `increasing` argument when calling `predict()` on a \pkg{parsnip} model object.