Option to add priors to posterior plots about see HOT 15 CLOSED

I don't think it's too hard. We could have an argument like show_priors or so, and then the layer for the prior can be completely independent from the remaining plot, it's just that we should add the prior layer first, and maybe add some alpha to both prior and posterior layer.

Here's a quick prototype:

library(see)
library(ggplot2)
library(bayestestR)
library(rstanarm)
model <- stan_glm(mpg ~ wt + am, data = mtcars, chains = 1)

p <- plot(point_estimate(model), panel = F)[[2]]
sp <- simulate_prior(model)
ed <- estimate_density(sp[["am"]])

p + geom_ribbon(
  data = ed,
  mapping = aes(
    x = x,
    ymin = 0,
    ymax = y,
    group = NA
  ),
  fill = "#FFC107",
  alpha = .3
)

^{Created on 2019-09-13 by the reprex package (v0.3.0)}

from see.

We should probably think of limiting the range of the x-axis, though, since the prior usually is very wide compared to the posterior.

from see.

Nice!

We should probably think of limiting the range of the x-axis,

This is the case for default priors, in which case people are anyway usually not really interested in displaying them anyway, but when people care about the priors I guess it makes sense to display them entirely (even if the plot is uglier)

from see.

And you can add xlim(c(-20, 20)) or similar easily by yourself...

from see.

but when people care about the priors I guess it makes sense to display them entirely

True, but you still might want to limit the x axis in some way - for example, if a Cauchy prior is used, some very very extreme samples can be drawn - which not only messes up the x-axis, it also messes up the density estimation (which is where logspline shines)...

See how I've dealt with this here (lines 377-390): https://github.com/easystats/bayestestR/blob/master/R/bayesfactor_parameters.R#L377

from see.

So you suggest limiting the range to +/- 7SD (or MAD)? Sounds feasible to me.

from see.

Yes, I suggest using MAD - SD gives a very wide window with Cauchy / t(df<3).

from see.

hm, doesn't geom_ridgeline_gradient an alpha option?

from see.

ok, geom_ridgeline() has, and I don't need geom_ridgeline_gradient() for the prior layer

from see.

It is probably better to add the prior layer onto the main layer for those plots where we have to use geom_ridgeline_gradient, because that geom allows no alpha.

from see.

I started adding the prior-feature to some of the plots:

https://easystats.github.io/see/articles/bayestestR.html

• Do we want the argument to be names show_priors or just priors?
• What about the other arguments, like priors_alpha?

In some cases we hard-code colors (e.g. point-estimate). We may think changing this somewhen in the future...

from see.

from see.

I like when args controlling a main arg stems out of it, so if we go for priors_alpha it makes sense to leave it just as priors = TRUE

from see.

from see.

I'm not sure whether there are any plots left to add priors? For HDI, we have the simple density plots, so I think we don't additionally need this option for HDI, do we?

from see.