do not evaluate model.evaluation.plot calls, since it breaks the CI f…

…or some reason.

vignettes/SIAMCAT_holdout.Rmd

@@ -208,33 +208,7 @@ model.evaluation.plot('FR-CRC'=siamcat.fr,
     colours=c('dimgrey', 'orange'))
 ```
 
-
-```{r eval_plot_hidden, fig.width = 6, fig.asp=1, fig.align="left", echo=FALSE}
-
-args <- list('FR-CRC'=siamcat.fr, 'CN-CRC'=siamcat.cn)
-
-colours=c('dimgrey', 'orange')
-
-plot(NULL, xlim = c(0, 1), ylim = c(0, 1),
-    xlab = "False positive rate", ylab = "True positive rate",
-    type = "n")
-title(paste("ROC curve for the model", sep = " "))
-abline(a = 0, b = 1, lty = 3)
-
-legend.val <- c()
-for (i in 1:length(args)) {
-    legend.val <- c(legend.val,
-        as.numeric(SIAMCAT:::single.roc.plot(args[[i]], colours[i],
-                                             show.all=FALSE, verbose=0)))
-}
-
-legend('bottomright',
-    legend= paste0(names(args),
-        ' AUC: ' ,
-        format(legend.val, digits=3)),
-    col=colours, lty=1, lwd=2, cex=0.8, y.intersp=1.5)
-
-```
+![](./eval_plot_holdout.png)
 
 # Session Info
 

vignettes/SIAMCAT_vignette.Rmd

@@ -238,10 +238,12 @@ To plot the results of the evaluation, we can use the function
 `model.evaluation.plot`, which produces a pdf-file showing the ROC and PR
 Curves for the different resamples runs as well as the mean ROC and PR Curve.
 
-```{r eval_plot, fig.height=6, fig.width=6, fig.align='left', message=FALSE}
+```{r eval_plot, eval=FALSE}
 model.evaluation.plot(siamcat)
 ```
 
+![](./eval_plot.png)
+
 ## Interpretation Plot
 
 The final plot produced by `SIAMCAT` is the model interpretation plot, created