diff --git a/inst/appdir/characterizationOfTasksBootstrapping.Rmd b/inst/appdir/characterizationOfTasksBootstrapping.Rmd index 93176c9..6177954 100644 --- a/inst/appdir/characterizationOfTasksBootstrapping.Rmd +++ b/inst/appdir/characterizationOfTasksBootstrapping.Rmd @@ -1,49 +1,49 @@ ### Visualizing bootstrap results To investigate which tasks separate algorithms well (i.e., lead to a stable ranking), a blob plot is recommended. Bootstrap results can be shown in a blob plot showing one plot for each task. In this view, the spread of the blobs for each algorithm can be compared across tasks. Deviations from the diagonal indicate deviations from the consensus ranking (over tasks). Specifically, if rank distribution of an algorithm is consistently below the diagonal, the algorithm performed better in this task than on average across tasks, while if the rank distribution of an algorithm is consistently above the diagonal, the algorithm performed worse in this task than on average across tasks. At the bottom of each panel, ranks for each algorithm in the tasks are provided. Same as in Section \ref{blobByTask} but now ordered according to consensus. \bigskip -```{r blobplot_bootstrap_byTask,fig.width=9, fig.height=9} +```{r blobplot_bootstrap_byTask,fig.width=9, fig.height=9, results='hide'} #stabilityByTask.bootstrap.list if (length(boot_object$matlist)<=6 &nrow((boot_object$matlist[[1]]))<=10 ){ stabilityByTask(boot_object, ordering=ordering_consensus, max_size = 9, size=4, shape=4) + scale_color_manual(values=cols) } else { pl=list() for (subt in names(boot_object$bootsrappedRanks)){ a=list(bootsrappedRanks=list(boot_object$bootsrappedRanks[[subt]]), matlist=list(boot_object$matlist[[subt]])) names(a$bootsrappedRanks)=names(a$matlist)=subt class(a)="bootstrap.list" r=boot_object$matlist[[subt]] pl[[subt]]=stabilityByTask(a, max_size = 9, ordering=ordering_consensus, size.ranks=.25*theme_get()$text$size, size=4, shape=4) + scale_color_manual(values=cols) + ggtitle(subt) } print(pl) } ``` \ No newline at end of file