Update loo2-lfo.Rmd

[ci skip]

Author: jgabry

vignettes/loo2-lfo.Rmd

@@ -240,16 +240,13 @@ df <- data.frame(
   time = 1:N
 )
 
-# save plot labels to reuse them
-plot_labs <- labs(
-  y = "Water Level (ft)", 
-  x = "Year",
-  title = "Water Level in Lake Huron (1875-1972)"
-) 
-
 ggplot(df, aes(x = year, y = y)) + 
   geom_point(size = 1) +
-  plot_labs
+  labs(
+    y = "Water Level (ft)", 
+    x = "Year",
+    title = "Water Level in Lake Huron (1875-1972)"
+  ) 
 ```
 
 The above plot shows rather strong autocorrelation of the time-series as well as
@@ -259,12 +256,11 @@ We can specify an AR(4) model for these data using the **brms** package as
 follows:
 
 ```{r fit, results = "hide"}
-control <- list(adapt_delta = 0.99)
 fit <- brm(
   y ~ ar(time, p = 4), 
   data = df, 
   prior = prior(normal(0, 0.5), class = "ar"),
-  control = control, 
+  control = list(adapt_delta = 0.99), 
   seed = SEED, 
   chains = CHAINS
 )
@@ -284,8 +280,12 @@ preds <- cbind(
 ggplot(cbind(df, preds), aes(x = year, y = Estimate)) +
   geom_smooth(aes(ymin = Q5, ymax = Q95), stat = "identity", size = 0.5) +
   geom_point(aes(y = y)) + 
-  labs(subtitle = "Mean (blue) and 90% predictive intervals (gray) vs. observed data (black)") +
-  plot_labs
+  labs(
+    y = "Water Level (ft)", 
+    x = "Year",
+    title = "Water Level in Lake Huron (1875-1972)",
+    subtitle = "Mean (blue) and 90% predictive intervals (gray) vs. observed data (black)"
+  ) 
 ```
 
 To allow for reasonable predictions of future values, we will require at least