minor changes

Author: GuyliannEngels

inst/tutorials/C01Lb_lda/C01Lb_lda.Rmd

@@ -1,10 +1,10 @@
 ---
 title: "Analyse discriminante linéaire"
 author: "Guyliann Engels & Philippe Grosjean"
-description: "**SDD III** Exercices sur l'ADL"
+description: "**SDD III Module 1** Exercices sur l'ADL"
 tutorial:
   id: "C01Lb_lda"
-  version: 1.1.1/5
+  version: 1.2.0/5
 output:
   learnr::tutorial:
     progressive: true
@@ -28,14 +28,14 @@ n_learning <- round(n * 2/3)
 set.seed(164)
 learning <- sample(1:n, n_learning)
 
-bio_test <- as.data.frame(bio[ -learning, ])
-bio_learn <- as.data.frame(bio[ learning, ])
+bio_test <- bio[ -learning, ]
+bio_learn <- bio[ learning, ]
 
 #bio_test <- slice(bio, -learning)
 #bio_learn <- slice(bio, learning)
 
 ## Creation d'un modèle lda
-bio_lda <- mlLda(formula = gender ~ ., data = bio_learn)
+bio_lda <- mlLda(data = bio_learn, gender ~ .)
 
 ## Confusion 
 bio_conf <- confusion(predict(bio_lda, bio_test), bio_test$gender)
@@ -86,20 +86,20 @@ table(bio_test$gender)
 Réalisez un modèle avec le set d'apprentissage. Prédisez la variable `gender` à l'aide des 3 variables numériques. 
 
 ```{r lda1_h2, exercise = TRUE}
-bio_lda <- mlLda(___ ~ ___, data = ___)
+bio_lda <- mlLda(data = ___, ___ ~ ___)
 summary(bio_lda)
 ```
 
 ```{r lda1_h2-hint-1}
-bio_lda <- mlLda(___ ~ ___, data = bio_learn)
+bio_lda <- mlLda(data = bio_learn, ___ ~ ___)
 summary(bio_lda)
 
 ## Attention, le prochain indice est la solution ##
 ```
 
 ```{r lda1_h2-solution}
 ## Solution ##
-bio_lda <- mlLda(gender ~ ., data = bio_learn)
+bio_lda <- mlLda(data = bio_learn, gender ~ .)
 summary(bio_lda)
 ```