hide output produced during model fitting in vignettes

vignettes/brms_multivariate.Rmd

@@ -47,7 +47,7 @@ head(BTdata)
 
 We begin with a relatively simple multivariate normal model.
 
-```{r fit1, message=FALSE, warning=FALSE}
+```{r fit1, message=FALSE, warning=FALSE, results='hide'}
 fit1 <- brm(
   cbind(tarsus, back) ~ sex + hatchdate + (1|p|fosternest) + (1|q|dam),
   data = BTdata, chains = 2, cores = 2
@@ -80,7 +80,7 @@ Clearly, there is much variation in both animal characteristics that we can not
 
 Now, suppose we only want to control for `sex` in `tarsus` but not in `back` and vice versa for `hatchdate`. Not that this is particular reasonable for the present example, but it allows us to illustrate how to specify different formulas for different response variables. We can no longer use `cbind` syntax and so we have to use a more verbose approach: 
 
-```{r fit2, message=FALSE, warning=FALSE}
+```{r fit2, message=FALSE, warning=FALSE, results='hide'}
 bf_tarsus <- bf(tarsus ~ sex + (1|p|fosternest) + (1|q|dam))
 bf_back <- bf(back ~ hatchdate + (1|p|fosternest) + (1|q|dam))
 fit2 <- brm(bf_tarsus + bf_back, data = BTdata, chains = 2, cores = 2)
@@ -103,7 +103,7 @@ Apparently, there is no noteworthy difference in the model fit. Accordingly, we
 
 To give you a glimpse of the capabilities of **brms**' multivariate syntax, we change our model in various directions at the same time. Remember the slight left skewness of `tarsus`, which we will now model by using the `skew_normal` family instead of the `gaussian` family. Since we do not have a multivariate normal (or student-t) model, anymore, estimating residual correlations is no longer possible. We make this explicit using the `set_rescor` function. Further, we investigate if the relationship of `back` and `hatchdate` is really linear as previously assumed by fitting a non-linear spline of `hatchdate`. On top of it, we model separate residual variances of `tarsus` for males and femals chicks.
 
-```{r fit3, message=FALSE, warning=FALSE}
+```{r fit3, message=FALSE, warning=FALSE, results='hide'}
 bf_tarsus <- bf(tarsus ~ sex + (1|p|fosternest) + (1|q|dam)) +
   lf(sigma ~ 0 + sex) + skew_normal()
 bf_back <- bf(back ~ s(hatchdate) + (1|p|fosternest) + (1|q|dam)) +