Fitted and predicted draws on average

Interfaces brms
1

I am struggling to wrap my head around the difference between fitted and predict. In my case, it is about the multinomial family. There are a few relevant posts here and on GitHub. After reading them and pp_expect.R with posterior_predict.R, my current understanding is that fitted gives draws of the expected value of the posterior distribution, while predict gives draws from the posterior distribution. How far am I from the truth? If not too far, should one expect the means of the two to match? If I take the average of the draws from fitted and the average of those from predict, should they not be the same? In my example, they deviate.

1 Like
2

You are correct. The deviation are likely because of random error in the posterior predictions.

3

Thank you for the quick reply. I guess I misuse some of the functions then. Let me give a minimal working example:

data <- tribble(
    ~a,   ~b,   ~c, ~total,
  3000, 3000, 7000,  13000,
)
data$counts <- with(data, cbind(a, b, c))
formula <- brmsformula(counts | trials(total) ~ 1)
fit <- brm(formula, data, multinomial(), seed = 42)

data$counts / data$total
#              a         b         c
# [1,] 0.2307692 0.2307692 0.5384615

colMeans(fitted(fit, summary = FALSE)) / data$total
#              a         b         c
# [1,] 0.2309553 0.2307284 0.5383163

colMeans(predict(fit, summary = FALSE)) / data$total
#              a         b         c
# [1,] 0.1364585 0.2725709 0.5909706
4

This difference cannot arguably be attributed to chance. I have tried with different seeds. In addition, if it was about chance, it would go away with the sample size.

5

I will take a look. Thanks!

6

Thanks! It turned out to be a slightly embarassing typo as I wrote pcategorical instead of dcategorical so accidentally used the cumulative distribution function… Should be fixed now on github.

7

That was fast! Thank you so much!