Update Description and add image alt text (#322)

* use ekables

* add email to description as well as new website

* try-dark

* copy from pkgdown site

* copy from pkgdown site

* alt text and bump date

* spell and rebuild

* where does lib come from anyways

* try rich google results

* rebuild

* new

* rename

* greedy

* ok

* rebuild

.Rbuildignore

@@ -31,3 +31,4 @@ check_package.R
 ^docs$
 ^pkgdown$
 ^README\.Rmd$
+lib/

DESCRIPTION

@@ -2,12 +2,12 @@ Package: caretEnsemble
 Type: Package
 Title: Ensembles of Caret Models
 Version: 4.0.0
-Date: 2024-08-12
+Date: 2024-08-13
 Authors@R: c(person(c("Zachary", "A."), "Deane-Mayer", role = c("aut", "cre", "cph"), email = "[email protected]"),
     person(c("Jared", "E.", "Knowles"), role="ctb", email="[email protected]"),
-    person("Antón", "López", role="ctb", email="[email protected]")
+    person("Antón", "López", role="ctb", email="[email protected]")
     )
-URL: https://github.com/zachmayer/caretEnsemble
+URL: http://zachmayer.github.io/caretEnsemble/, https://github.com/zachmayer/caretEnsemble
 BugReports: https://github.com/zachmayer/caretEnsemble/issues
 Description: Functions for creating ensembles of caret models: caretList()
     and caretStack(). caretList() is a convenience function for fitting multiple

Makefile

@@ -99,6 +99,7 @@ check:
 
 .PHONY: check-win
 check-win:
+	rm -rf lib/
 	Rscript -e "devtools:::check_win()"
 
 .PHONY: fix-style
@@ -150,7 +151,7 @@ dev-guide:
 clean:
 	rm -rf *.Rcheck
 	rm -f *.tar.gz
-	rm -f man/*.Rd
+	rm -rf man/
 	rm -f README.md
 	rm -f coverage.rds
 	rm -f cobertura.xml

README.md

@@ -46,10 +46,10 @@ print(summary(models))
 #> The following models were ensembled: rf, glmnet  
 #> 
 #> Model accuracy:
-#>    model_name metric    value        sd
-#>        <char> <char>    <num>     <num>
-#> 1:         rf   RMSE 1127.974  83.50596
-#> 2:     glmnet   RMSE 1138.137 202.80472
+#>    model_name metric    value       sd
+#>        <char> <char>    <num>    <num>
+#> 1:         rf   RMSE 1168.108 422.9558
+#> 2:     glmnet   RMSE 1152.298 138.8310
 ```
 
 Then, use caretEnsemble to make a greedy ensemble of these models
@@ -67,23 +67,20 @@ print(greedy_stack)
 #> Summary of sample sizes: 400, 400, 400, 400, 400 
 #> Resampling results:
 #> 
-#>   RMSE      Rsquared   MAE     
-#>   1003.391  0.9357974  577.2895
+#>   RMSE     Rsquared  MAE    
+#>   1096.53  0.933482  631.215
 #> 
 #> Tuning parameter 'max_iter' was held constant at a value of 100
 #> 
 #> Final model:
 #> Greedy MSE
-#> RMSE:  1010.776 
+#> RMSE:  1067.635 
 #> Weights:
 #>        [,1]
-#> rf     0.52
-#> glmnet 0.48
-ggplot2::autoplot(greedy_stack, training_data = dat, xvars = c("carat", "table"))
+#> rf     0.43
+#> glmnet 0.57
 ```
 
-<img src="man/figures/README-unnamed-chunk-3-1.png" width="100%" />
-
 You can also use caretStack to make a non-linear ensemble
 
 ``` r
@@ -101,7 +98,7 @@ print(rf_stack)
 #> Resampling results:
 #> 
 #>   RMSE      Rsquared   MAE     
-#>   894.1208  0.9518165  455.1419
+#>   1005.629  0.9423501  490.6683
 #> 
 #> Tuning parameter 'mtry' was held constant at a value of 2
 #> 
@@ -113,12 +110,23 @@ print(rf_stack)
 #>                      Number of trees: 500
 #> No. of variables tried at each split: 2
 #> 
-#>           Mean of squared residuals: 800137.3
-#>                     % Var explained: 94.96
+#>           Mean of squared residuals: 1007817
+#>                     % Var explained: 94.29
+```
+
+Use autoplot from ggplot2 to plot ensemble diagnostics:
+
+``` r
+ggplot2::autoplot(greedy_stack, training_data = dat, xvars = c("carat", "table"))
+```
+
+<img src="man/figures/README-greedy-stack-6-plot-1.png" alt="6 panel plot of an ensemble of models fit to the diamonds dataset. The RF model is the best and has the highest weight. The residual plots look good. RMSE is about `r round(min(greedy_stack$ens_model$results$RMSE))`." width="100%" />
+
+``` r
 ggplot2::autoplot(rf_stack, training_data = dat, xvars = c("carat", "table"))
 ```
 
-<img src="man/figures/README-unnamed-chunk-4-1.png" width="100%" />
+<img src="man/figures/README-unnamed-chunk-5-1.png" alt="6 panel plot of an ensemble of models fit to the diamonds dataset. The RF model is the best and has the highest weight. The residual plots look good. RMSE is about `r round(min(rf_stack$ens_model$results$RMSE))`." width="100%" />
 
 # Installation
 

README.rmd

@@ -54,13 +54,20 @@ Then, use caretEnsemble to make a greedy ensemble of these models
 ```{r}
 greedy_stack <- caretEnsemble::caretEnsemble(models)
 print(greedy_stack)
-ggplot2::autoplot(greedy_stack, training_data = dat, xvars = c("carat", "table"))
 ```
 
 You can also use caretStack to make a non-linear ensemble
 ```{r}
 rf_stack <- caretEnsemble::caretStack(models, method = "rf")
 print(rf_stack)
+```
+
+Use autoplot from ggplot2 to plot ensemble diagnostics:
+```{r greedy-stack-6-plot, fig.alt="6 panel plot of an ensemble of models fit to the diamonds dataset. The RF model is the best and has the highest weight. The residual plots look good. RMSE is about `r round(min(greedy_stack$ens_model$results$RMSE))`."}
+ggplot2::autoplot(greedy_stack, training_data = dat, xvars = c("carat", "table"))
+```
+
+```{r, fig.alt="6 panel plot of an ensemble of models fit to the diamonds dataset. The RF model is the best and has the highest weight. The residual plots look good. RMSE is about `r round(min(rf_stack$ens_model$results$RMSE))`."}
 ggplot2::autoplot(rf_stack, training_data = dat, xvars = c("carat", "table"))
 ```
 

_pkgdown.yml

@@ -1,4 +1,14 @@
 url: http://zachmayer.github.io/caretEnsemble/
 template:
   bootstrap: 5
+  light-switch: true
+  bslib:
+    primary: "#0054AD"
+    border-radius: 0.5rem
+    btn-border-radius: 0.25rem
+    danger: "#A6081A"
+  opengraph:
+    image:
+      src: man/figures/README-greedy-stack-6-plot.png
+      alt: "6 panel plot of an ensemble of models fit to the diamonds dataset. The RF model is the best and has the highest weight. The residual plots look good."
 

inst/WORDLIST

@@ -10,6 +10,7 @@ CMD
 CodeFactor
 codebase
 coercible
+dat
 Deane
 defaultControl
 dev
@@ -51,12 +52,14 @@ prob
 probs
 readme
 resid
+rf
 roxygen
 rpart
 's
 savePredictions
 scalability
 scikit
+setosa
 SDs
 trainControl
 travis

vignettes/Version-4.0-New-Features.Rmd

@@ -1,5 +1,5 @@
 ---
-title: "Version-4.0-New-Features"
+title: "Version 4.0 New Features"
 author: "Zach Deane-Mayer"
 date: "`r Sys.Date()`"
 output: rmarkdown::html_vignette
@@ -45,7 +45,13 @@ caretStack (and by extension, caretEnsemble) now supports various S3 methods:
 ```{r}
 print(ens)
 print(summary(ens))
+```
+
+```{r, fig.alt="A dot and whisker plot of ROC for glmnet, rpart, and an ensemble. The ensemble has the highest ROC and is slighly better than the glmnet. The rpart model is bad."}
 plot(ens)
+```
+
+```{r, fig.alt="A 4-panel plot for glmnet, rpart, and an ensemble. The ensemble has the highest ROC and is slighly better than the glmnet. The rpart model is bad. The glmnet has the highest weight, and the residuals look biased."}
 ggplot2::autoplot(ens)
 ```
 
@@ -127,14 +133,16 @@ print(transfer_ens)
 We can also predict on new data:
 ```{r}
 preds <- predict(transfer_ens, newdata = head(new_data))
-print(preds)
+knitr::kable(preds, format = "markdown")
 ```
 
 # Permutation Importance
 Permutation importance is now the default method for variable importance in caretLists and caretStacks:
 ```{r}
 importance <- caret::varImp(transfer_ens)
-print(importance)
+print(round(importance, 2L))
 ```
 
+Note that the ensemble uses rpart to classify the easy class (setosa) and then uses the rf to distinguish between the 2 more difficult classes.
+
 This completes our demonstration of the key new features in caretEnsemble 4.0. These enhancements provide greater flexibility, improved performance, and easier usage for ensemble modeling in R.

vignettes/caretEnsemble-intro.Rmd

@@ -49,7 +49,7 @@ print(summary(model_list))
 We can use the `predict` function to extract predictions from this object for new data:
 ```{r}
 p <- predict(model_list, newdata = head(testing))
-print(p)
+knitr::kable(p, format = "markdown")
 ```
 
 If you desire more control over the model fit, use the `caretModelSpec` to construct a list of model specifications for the `tuneList` argument. This argument can be used to fit several different variants of the same model, and can also be used to pass arguments through `train` down to the component functions (e.g. `trace=FALSE` for `nnet`):
@@ -71,7 +71,7 @@ Finally, you should note that `caretList` does not support custom caret models.
 
 ## caretEnsemble
 `caretList` is the preferred way to construct list of caret models in this package, as it will ensure the resampling indexes are identical across all models. Lets take a closer look at our list of models:
-```{r}
+```{r, fig.alt="X/Y scatter plot of rpart vs glmnet AUCs on the Sonar dataset. The glmnet model is better for 4 out of 5 resamples."}
 lattice::xyplot(caret::resamples(model_list))
 ```
 
@@ -100,7 +100,8 @@ The ensemble has an AUC on the training set resamples of `r round(auc[1, 'ensemb
 
 Note that the levels for the Sonar Data are "M" and "R", where M is level 1 and R is level 2. "M" stands for "metal cylinder" and "R" stands for rock. M is the positive class, so we exclude class 2L from our predictions. You can set excluded_class_id = 0L
 ```{r}
-predict(greedy_ensemble, newdata = head(testing), excluded_class_id = 0L)
+p <- predict(greedy_ensemble, newdata = head(testing), excluded_class_id = 0L)
+knitr::kable(p, format = "markdown")
 ```
 
 We can also use varImp to extract the variable importances from each member of the ensemble, as well as the final ensemble model: