fix yaml, edit for brevity

README.Rmd

@@ -62,9 +62,7 @@ remotes::install_github("ropensci/aorsf")
 
 ## What is an oblique decision tree?
 
-Decision trees are developed by splitting a set of training data into two new subsets, with the goal of having more similarity within the new subsets than between them. The splitting process is repeated on resulting subsets of data until a stopping criterion is met. 
-
-When the new subsets of data are formed based on a single predictor, the decision tree is said to be *axis-based* because the splits of the data appear perpendicular to the axis of the predictor. When linear combinations of variables are used instead of a single variable, the tree is *oblique* because the splits of the data are neither parallel nor at a right angle to the axis. 
+Decision trees are grown by splitting a set of training data into non-overlapping subsets, with the goal of having more similarity within the new subsets than between them. When subsets are created with a single predictor, the decision tree is *axis-based* because the subset boundaries are perpendicular to the axis of the predictor. When linear combinations (i.e., a weighted sum) of variables are used instead of a single variable, the tree is *oblique* because the boundaries are neither parallel nor perpendicular to the axis. 
 
 **Figure**: Decision trees for classification with axis-based splitting (left) and oblique splitting (right). Cases are orange squares; controls are purple circles. Both trees partition the predictor space defined by variables X1 and X2, but the oblique splits do a better job of separating the two classes.
 

README.md

@@ -52,17 +52,14 @@ remotes::install_github("ropensci/aorsf")
 
 ## What is an oblique decision tree?
 
-Decision trees are developed by splitting a set of training data into
-two new subsets, with the goal of having more similarity within the new
-subsets than between them. The splitting process is repeated on
-resulting subsets of data until a stopping criterion is met.
-
-When the new subsets of data are formed based on a single predictor, the
-decision tree is said to be *axis-based* because the splits of the data
-appear perpendicular to the axis of the predictor. When linear
-combinations of variables are used instead of a single variable, the
-tree is *oblique* because the splits of the data are neither parallel
-nor at a right angle to the axis.
+Decision trees are grown by splitting a set of training data into
+non-overlapping subsets, with the goal of having more similarity within
+the new subsets than between them. When subsets are created with a
+single predictor, the decision tree is *axis-based* because the subset
+boundaries are perpendicular to the axis of the predictor. When linear
+combinations (i.e., a weighted sum) of variables are used instead of a
+single variable, the tree is *oblique* because the boundaries are
+neither parallel nor perpendicular to the axis.
 
 **Figure**: Decision trees for classification with axis-based splitting
 (left) and oblique splitting (right). Cases are orange squares; controls
@@ -112,10 +109,10 @@ fit
 #>                  N trees: 500
 #>       N predictors total: 17
 #>    N predictors per node: 5
-#>  Average leaves per tree: 10.198
+#>  Average leaves per tree: 10.238
 #> Min observations in leaf: 5
 #>       Min events in leaf: 1
-#>           OOB stat value: 0.84
+#>           OOB stat value: 0.83
 #>            OOB stat type: Harrell's C-index
 #>      Variable importance: anova
 #> 
@@ -149,14 +146,12 @@ using `aorsf`:
   ``` r
 
   orsf_vi_negate(fit)
-  #>          bili           sex        copper         stage           age 
-  #>  0.1152040355  0.0550384871  0.0346540451  0.0342394602  0.0212811906 
-  #>           ast       protime        hepato          chol       albumin 
-  #>  0.0189147173  0.0179113542  0.0151705980  0.0111316204  0.0107352274 
-  #>         edema       ascites       spiders          trig      alk.phos 
-  #>  0.0101276579  0.0097629480  0.0043991365  0.0031611867  0.0030722039 
-  #>           trt      platelet 
-  #>  0.0024625381 -0.0005569787
+  #>         bili          sex       copper        stage          age          ast 
+  #>  0.117180683  0.058528338  0.033761789  0.026655509  0.022144911  0.019139095 
+  #>      protime       hepato        edema      ascites      albumin         chol 
+  #>  0.016879701  0.011605852  0.010634489  0.009580159  0.008336260  0.007633992 
+  #>          trt      spiders     alk.phos         trig     platelet 
+  #>  0.002705027  0.002662017  0.002413369  0.001197399 -0.003386483
   ```
 
 - **permutation**: Each variable is assessed separately by randomly
@@ -170,14 +165,12 @@ using `aorsf`:
   ``` r
 
   orsf_vi_permute(fit)
-  #>          bili         stage        copper           age           sex 
-  #>  0.0487808290  0.0180334035  0.0178325263  0.0124277288  0.0111897854 
-  #>           ast        hepato         edema       protime       ascites 
-  #>  0.0111084077  0.0095618267  0.0082559886  0.0082473977  0.0078725536 
-  #>       albumin          chol       spiders      alk.phos          trig 
-  #>  0.0073437819  0.0057881417  0.0033941136  0.0028194077  0.0019933887 
-  #>      platelet           trt 
-  #> -0.0002101522 -0.0016860907
+  #>         bili       copper          age        stage          sex          ast 
+  #>  0.050536719  0.016394807  0.013793348  0.013204760  0.010261860  0.010101841 
+  #>        edema      ascites      protime      albumin       hepato         chol 
+  #>  0.008298456  0.008148291  0.007630773  0.006667768  0.006141770  0.002881687 
+  #>      spiders         trig     alk.phos     platelet          trt 
+  #>  0.001669604  0.001047642 -0.000301684 -0.001417230 -0.001665785
   ```
 
 - **analysis of variance (ANOVA)**<sup>3</sup>: A p-value is computed
@@ -193,12 +186,12 @@ using `aorsf`:
   ``` r
 
   orsf_vi_anova(fit)
-  #>       bili    ascites      edema        sex     copper      stage        age 
-  #> 0.48004315 0.43536122 0.38654727 0.31024531 0.29493088 0.27168950 0.26673985 
-  #>     hepato        ast    albumin    protime       chol    spiders       trig 
-  #> 0.21492921 0.20151679 0.18771331 0.18352060 0.14617169 0.13932292 0.13620489 
-  #>   alk.phos   platelet        trt 
-  #> 0.09051254 0.07011494 0.06179067
+  #>         bili       copper          age        stage          sex          ast 
+  #>  0.050536719  0.016394807  0.013793348  0.013204760  0.010261860  0.010101841 
+  #>        edema      ascites      protime      albumin       hepato         chol 
+  #>  0.008298456  0.008148291  0.007630773  0.006667768  0.006141770  0.002881687 
+  #>      spiders         trig     alk.phos     platelet          trt 
+  #>  0.001669604  0.001047642 -0.000301684 -0.001417230 -0.001665785
   ```
 
 You can supply your own R function to estimate out-of-bag error when
@@ -224,16 +217,17 @@ orsf_summarize_uni(fit, n_variables = 2)
 #> 
 #>        |---------------- Risk ----------------|
 #>  Value      Mean    Median     25th %    75th %
-#>   0.70 0.2099215 0.1262483 0.05057666 0.3160044
-#>   1.30 0.2244830 0.1472311 0.06196578 0.3369922
-#>   3.18 0.2932736 0.2263620 0.11896921 0.4424589
+#>   0.70 0.2043124 0.1288782 0.05502854 0.3130744
+#>   1.30 0.2193531 0.1430383 0.06680735 0.3352729
+#>   3.18 0.2835984 0.2210419 0.12363028 0.4313679
 #> 
-#> -- ascites (VI Rank: 2) ------------------------
+#> -- copper (VI Rank: 2) -------------------------
 #> 
 #>        |---------------- Risk ----------------|
-#>  Value      Mean    Median    25th %    75th %
-#>      0 0.2630294 0.1490837 0.0613327 0.4186542
-#>      1 0.3924844 0.3053928 0.2222267 0.5253717
+#>  Value      Mean    Median     25th %    75th %
+#>   39.0 0.2308500 0.1358346 0.05536305 0.3575617
+#>   68.0 0.2415171 0.1482876 0.06189812 0.3682164
+#>    111 0.2725110 0.1846062 0.08723814 0.4047750
 #> 
 #>  Predicted risk at time t = 1826.25 for top 2 predictors
 ```
@@ -266,7 +260,7 @@ Comparisons between `aorsf` and existing software are presented in our
   learners.
 
 - runs a simulation study comparing variable importance techniques with
-  ORSFs, axis based RSFs, and boosted trees.
+  oblique survival RFs, axis based survival RFs, and boosted trees.
 
 - reports the probability that each variable importance technique will
   rank a relevant variable with higher importance than an irrelevant
@@ -295,9 +289,9 @@ examples](https://docs.ropensci.org/aorsf/reference/orsf.html#tidymodels)
 
 ## Funding
 
-The developers of `aorsf` receive financial support from the Center for
+The developers of `aorsf` received financial support from the Center for
 Biomedical Informatics, Wake Forest University School of Medicine. We
-also receive support from the National Center for Advancing
+also received support from the National Center for Advancing
 Translational Sciences of the National Institutes of Health under Award
 Number UL1TR001420.
 

_pkgdown.yml

@@ -42,6 +42,7 @@ reference:
 - contents:
   - matches("^orsf_ice")
   - matches("^orsf_pd")
+  - matches("^pred_spec")
 - title: Example survival data
   desc: Datasets used in examples and vignettes.
 - contents: