Merge pull request #20 from Yue-Jiang/master

bug fix inline code doesn't print, fixes #19

inst/templates/Explain_forest_template.Rmd

@@ -81,15 +81,15 @@ if(inherits(forest, "randomForest")){
 plot_multi_way_importance(importance_frame, size_measure = "no_of_nodes")
 ```
 
-The second multi-way importance plot shows importance measures that derive from the role a variable plays in prediction: `r print(noquote(measures_print))` with the additional information on the $p$-value based on a binomial distribution of the number of nodes split on the variable assuming that variables are randomly drawn to form splits (i.e. if a variable is significant it means that the variable is used for splitting more often than would be the case if the selection was random).
+The second multi-way importance plot shows importance measures that derive from the role a variable plays in prediction: `r measures_print` with the additional information on the $p$-value based on a binomial distribution of the number of nodes split on the variable assuming that variables are randomly drawn to form splits (i.e. if a variable is significant it means that the variable is used for splitting more often than would be the case if the selection was random).
 
 ```{r}
 plot_multi_way_importance(importance_frame, x_measure = measure1, y_measure = measure2, size_measure = "p_value")
 ```
 
 ## Compare importance measures
 
-The plot below shows bilateral relations between the following importance measures: `r print(noquote(paste(measures, collapse = ", ")))`, if some variables are strongly related to each other it may be worth to consider focusing only on one of them. 
+The plot below shows bilateral relations between the following importance measures: `r paste(measures, collapse = ", ")`, if some variables are strongly related to each other it may be worth to consider focusing only on one of them. 
 
 ```{r, fig.width = 10, fig.height = 9}
 plot_importance_ggpairs(importance_frame, measures) + theme_set(theme_bw(13))

inst/templates/Explain_forest_template_interactions.Rmd

@@ -12,7 +12,7 @@ Note that:
 
 - the horizontal line shows the minimal value of the depicted statistic among interactions for which it was calculated,
 
-- the interactions considered are ones with the following variables as first (root variables): `r print(noquote(paste(vars, collapse = ", ")))` and all possible values of the second variable.
+- the interactions considered are ones with the following variables as first (root variables): `r paste(vars, collapse = ", ")` and all possible values of the second variable.
 
 ```{r}
 plot_min_depth_interactions(interactions_frame)