try with cache.lazy=FALSE

15-eco.Rmd

@@ -303,7 +303,7 @@ nmds$stress
 saveRDS(nmds, "extdata/15-nmds.rds")
 ```
 
-```{r 15-eco-14, include=FALSE}
+```{r 15-eco-14, include=FALSE, cache.lazy=FALSE}
 nmds = readRDS("extdata/15-nmds.rds")
 ```
 
@@ -326,7 +326,7 @@ plot(y = sc[, 1], x = elev, xlab = "elevation in m",
      ylab = "First NMDS axis", cex.lab = 0.8, cex.axis = 0.8)
 ```
 
-```{r xy-nmds, fig.cap="Plotting the first NMDS axis against altitude.", fig.scap = "First NMDS axis against altitude plot.", fig.asp=1, out.width="60%", message=FALSE, echo=FALSE}
+```{r xy-nmds, fig.cap="Plotting the first NMDS axis against altitude.", fig.scap = "First NMDS axis against altitude plot.", fig.asp=1, out.width="60%", message=FALSE, echo=FALSE, cache.lazy=FALSE}
 elev = dplyr::filter(random_points, id %in% rownames(pa)) |> 
   dplyr::pull(dem)
 # rotating NMDS in accordance with altitude (proxy for humidity)
@@ -399,7 +399,7 @@ We refer the reader to @james_introduction_2013 for a more detailed description
 To introduce decision trees by example, we first construct a response-predictor matrix by joining the rotated NMDS\index{NMDS} scores to the field observations (`random_points`).
 We will also use the resulting data frame for the **mlr3**\index{mlr3 (package)} modeling later on.
 
-```{r 15-eco-16, message=FALSE}
+```{r 15-eco-16, message=FALSE, cache.lazy=FALSE}
 # construct response-predictor matrix
 # id- and response variable
 rp = data.frame(id = as.numeric(rownames(sc)), sc = sc[, 1])