use italics

Author: jannes

12-spatial-cv.Rmd

@@ -288,7 +288,7 @@ Third, the **resampling** approach assesses the predictive performance of the mo
 ### Generalized linear model {#glm}
 
 To use a GLM\index{GLM} in **mlr3**\index{mlr3 (package)}, we must create a **task** containing the landslide data.
-Since the response is binary (two-category variable) and has a spatial dimension, we create a classification\index{classification} task with `as_task_classif_st()` of the **mlr3spatiotempcv** package [@schratz_mlr3spatiotempcv_2021, for non-spatial tasks, use `mlr3::as_task_classif()` or `mlr3::as_task_regr()` for regression\index{regression} tasks, see `?Task` for other task types].^[The **mlr3** ecosystem makes use of **data.table** and **R6** classes. And though you might use **mlr3** without knowing the specifics of **data.table** or **R6**, it might be rather helpful. To learn more about **data.table**, please refer to https://rdatatable.gitlab.io/data.table/. To learn more about **R6**, we recommend [Chapter 14](https://adv-r.hadley.nz/fp.html) of the Advanced R book [@wickham_advanced_2019].]
+Since the response is binary (two-category variable) and has a spatial dimension, we create a classification\index{classification} task with `as_task_classif_st()` of the **mlr3spatiotempcv** package [@schratz_mlr3spatiotempcv_2021, for non-spatial tasks, use `mlr3::as_task_classif()` or `mlr3::as_task_regr()` for regression\index{regression} tasks, see `?Task` for other task types].^[The **mlr3** ecosystem makes use of **data.table** and **R6** classes. And though you might use **mlr3** without knowing the specifics of **data.table** or **R6**, it might be rather helpful. To learn more about **data.table**, please refer to https://rdatatable.gitlab.io/data.table/. To learn more about **R6**, we recommend [Chapter 14](https://adv-r.hadley.nz/fp.html) of the *Advanced R* book [@wickham_advanced_2019].]
 The first essential argument of these `as_task_` functions is `x`.
 `x` expects that the input data includes the response and predictor variables.
 The `target` argument indicates the name of a response variable (in our case this is `lslpts`) and `positive` determines which of the two factor levels of the response variable indicate the landslide initiation point (in our case this is `TRUE`).