Update ComponentModel.Rmd

ComponentModel.Rmd

@@ -104,7 +104,7 @@ ggplot(Viscosity_quality) +
     geom_point(aes(x = R.squared, y = AIC, colour = as.factor(n_terms)))
 ```
 
-#finally, the selection of the right model. This is tricky. Let's arrange the models by $R^2$ and AIC.
+#Arrange the models by $R^2$ and AIC.
 ```{r}
 R2_arranged <- Viscosity_quality %>%
     arrange(desc(R.squared))
@@ -122,7 +122,7 @@ AIC_arranged
 # Print the summary of the 5 best models by AIC.
 ```{r}
 
-print_models_AIC <- lapply(1:2, function(x) {
+print_models_AIC <- lapply(1:5, function(x) {
           my_models <- Viscosity_models[[AIC_arranged$n_terms[x]]]
           models_AIC <- lapply(my_models, AIC)
           my_index <- which(AIC_arranged$AIC[x] == models_AIC)
@@ -172,7 +172,7 @@ lapply(1:1, function(x) {
     
 })
 ```
-#Plot parity plot
+#Plot parity plot best model according to AIC
 ```{r}
 lapply(1:1, function(x){
     my_models <- Viscosity_models[[AIC_arranged$n_terms[x]]]