Update 03-regression-regularisation.Rmd

_episodes_rmd/03-regression-regularisation.Rmd

@@ -528,15 +528,15 @@ When $\lambda$ is small, we don't really care a lot about shrinking our coeffici
 just using ordinary least squares. We see how a penalty term, $\lambda$, might be chosen later in this episode.
 
 For now, to see how regularisation might improve a model, let's fit a model using the same set
-of 20 features (stored as `features`) selected earlier in this episode (these
+of 20 features (stored as `cpg_markers`) selected earlier in this episode (these
 are a subset of the features identified by Horvarth et al), using both 
 regularised and ordinary least squares. To fit regularised regression models, we will use the **`glmnet`** package.
 
 ```{r plot-ridge, fig.cap="A line plot of coefficient estimates against log lambda for a ridge regression model.", fig.alt="A line plot of coefficient estimates against log lambda for a ridge regression model. Lines are depicted in different colours, with coefficients generally having large values on the left of the plot (small log lambda) and moving smoothly and gradually towards zero to the right of the plot (large log lambda). Some coefficients appear to increase and then decrease in magnitude as lambda increases, or switch signs."}
 library("glmnet")
 
 ## glmnet() performs scaling by default, supply un-scaled data:
-horvath_mat <- methyl_mat[, features] # select the first 20 sites as before
+horvath_mat <- methyl_mat[, cpg_markers] # select the same 20 sites as before
 train_mat <- horvath_mat[train_ind, ] # use the same individuals as selected before
 test_mat <- horvath_mat[-train_ind, ]