You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
McElreath encouraged us to "explore different values for `pbar` and `theta`" (p. 348). Here's a grid of plots with `pbar = c(.25, .5, .75)` and `theta = c(5, 10, 15)`
1031
+
McElreath encouraged us to "explore different values for `pbar` and `theta`" (p. 348). Here's a grid of plots with `pbar = c(.25, .5, .75)` and `theta = c(5, 10, 15)`.
1032
1032
1033
1033
```{r, fig.width = 5, fig.height = 5}
1034
1034
# data
@@ -1056,12 +1056,14 @@ If you'd like to see how to make a similar plot in terms of $\alpha$ and $\beta$
1056
1056
1057
1057
But remember, we're not fitting a beta model. We're using the beta-binomial. "We're going to bind our linear model to $\bar p$, so that changes in predictor variables change the central tendency of the distribution" (p. 348). The statistical model we'll be fitting follows the form
Here the size $n = \text{applications}$. In case you're confused, yes, our statistical model is not the one McElreath presented at the top of page 348 in the text. If you look closely, the statistical formula he presented does not match up with the one implied by his R code 11.26. Our statistical formula and the `brm()` model we'll be fitting, below, correspond to his R code 11.26.
1067
1069
@@ -1274,8 +1276,8 @@ ggplot(data = tibble(x = seq(from = 0, to = 12, by = .01)),
0 commit comments