Hello,

First, thank you for BRMS and all the underlying work. I’m hoping to get some clarification of why my predictions are noisy when I would expect them to be linear? (sorry if I’m missing something obvious) I am working on a multi-output model but my questions is focused on one of the levels which would apply to all ultimately. First, my data is a time series data set of health spending from many countries with many years of missing data in some countries and nearly complete data in others. My base model simply fits the log of spending with year.

`ln(health_exp) ~ year

fit <- brm(ln_value_pc ~ year, data = data_small_total)

Family: gaussian

Links: mu = identity; sigma = identity

Formula: ln_che_pc ~ year

Data: data_small_total (Number of observations: 1340)

Samples: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;

total post-warmup samples = 4000

ICs: LOO = NA; WAIC = NA; R2 = NA

Population-Level Effects:

Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat

Intercept 29.90 18.16 -5.75 64.87 4000 1.00

year -0.01 0.01 -0.03 0.01 4000 1.00

Family Specific Parameters:

Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat

sigma 1.88 0.04 1.82 1.96 3747 1.00

Samples were drawn using sampling(NUTS). For each parameter, Eff.Sample

is a crude measure of effective sample size, and Rhat is the potential

scale reduction factor on split chains (at convergence, Rhat = 1).

`

I would expect to simply predict a linear trend when I use predict to fill in all the years of data across my years. However, when using predict or posterior.predict the within country annual variation does not follow a simply linear trend. Below is the head of these predictions:

`

pred_che_fit <- predict(fit_che, newdata = data_small_total, allow_new_levels = TRUE, sort = TRUE)

Estimate Est.Error 2.5%ile 97.5%ile

1: 7.306073 1.411603 4.515836 9.929672

2: 7.343608 1.411364 4.573684 10.066197

3: 7.345000 1.411676 4.510049 10.068934

4: 7.359616 1.415086 4.641277 10.099347

5: 7.323695 1.410809 4.547681 10.137092

6: 7.349641 1.409937 4.639661 10.217046

`

Again, my interest is why are the above estimates and percentiles going up and down over year when it should be linear?

Thanks in advance! Matt

- Operating System: Using Rstudio on a multi core cluster computer
- brms Version: 2.2.0