Hi everyone,

First time using model averaging. I’m finding some unusual warnings during pp_average procedures, with brms models:

pp_average(m3.1.2.3.1_logs,m3.1.2.3.2_logs,

- m3.1.2.3.4_logs,m3.1.2.3_logs,m3.1.2.3.3_logs, method=“fitted”,newdata=df)

Warning messages:

1: Some Pareto k diagnostic values are too high. See help(‘pareto-k-diagnostic’) for details.2: Some Pareto k diagnostic values are too high. See help(‘pareto-k-diagnostic’) for details.

3: In log(z) : NaNs produzidos

4: Some Pareto k diagnostic values are too high. See help(‘pareto-k-diagnostic’) for details.5: Some Pareto k diagnostic values are too high. See help(‘pareto-k-diagnostic’) for details.

6: Some Pareto k diagnostic values are too high. See help(‘pareto-k-d> Blockquote

agnostic’) for details.

The individual loo results from models have good Pareto k estimates (k<0.7),

as can be seen:

loo.m3.1.2.3.1_logs

Computed from 20000 by 216 log-likelihood matrix

`Estimate SE`

## elpd_loo 225.0 16.5

p_loo 29.3 6.2

looic -449.9 33.0Monte Carlo SE of elpd_loo is NA.

Pareto k diagnostic values:

Count Pct. Min. n_eff

(-Inf, 0.5] (good) 209 96.8% 9

(0.5, 0.7] (ok) 7 3.2% 601

(0.7, 1] (bad) 0 0.0%

(1, Inf) (very bad) 0 0.0%All Pareto k estimates are ok (k < 0.7).

See help(‘pareto-k-diagnostic’) for details.loo.m3.1.2.3.2_logs

Computed from 20000 by 216 log-likelihood matrix

`Estimate SE`

## elpd_loo 230.8 18.6

p_loo 31.4 8.7

looic -461.7 37.2Monte Carlo SE of elpd_loo is NA.

Pareto k diagnostic values:

Count Pct. Min. n_eff

(-Inf, 0.5] (good) 210 97.2% 3

(0.5, 0.7] (ok) 6 2.8% 435

(0.7, 1] (bad) 0 0.0%

(1, Inf) (very bad) 0 0.0%All Pareto k estimates are ok (k < 0.7).

See help(‘pareto-k-diagnostic’) for details.loo.m3.1.2.3.3_logs

Computed from 20000 by 216 log-likelihood matrix

`Estimate SE`

## elpd_loo 226.1 17.7

p_loo 22.0 5.0

looic -452.2 35.5Monte Carlo SE of elpd_loo is 0.2.

Pareto k diagnostic values:

Count Pct. Min. n_eff

(-Inf, 0.5] (good) 211 97.7% 30

(0.5, 0.7] (ok) 5 2.3% 989

(0.7, 1] (bad) 0 0.0%

(1, Inf) (very bad) 0 0.0%All Pareto k estimates are ok (k < 0.7).

See help(‘pareto-k-diagnostic’) for details.loo.m3.1.2.3.4_logs

Computed from 20000 by 216 log-likelihood matrix

`Estimate SE`

## elpd_loo 231.3 19.3

p_loo 32.4 9.4

looic -462.5 38.5Monte Carlo SE of elpd_loo is 0.4.

Pareto k diagnostic values:

Count Pct. Min. n_eff

(-Inf, 0.5] (good) 208 96.3% 9

(0.5, 0.7] (ok) 8 3.7% 662

(0.7, 1] (bad) 0 0.0%

(1, Inf) (very bad) 0 0.0%All Pareto k estimates are ok (k < 0.7).

See help(‘pareto-k-diagnostic’) for details.loo.m3.1.2.3_logs

Computed from 20000 by 216 log-likelihood matrix

`Estimate SE`

## elpd_loo 223.7 17.7

p_loo 23.2 5.1

looic -447.4 35.5Monte Carlo SE of elpd_loo is 0.2.

Pareto k diagnostic values:

Count Pct. Min. n_eff

(-Inf, 0.5] (good) 209 96.8% 21

(0.5, 0.7] (ok) 7 3.2% 525

(0.7, 1] (bad) 0 0.0%

(1, Inf) (very bad) 0 0.0%All Pareto k estimates are ok (k < 0.7).

See help(‘pareto-k-diagnostic’) for details.

This models represent different patterns (intercept only, linear, categorical and additive) of a response variable along distances through a forest edge.

Does anyone have an idea of what was my mistake?

Thanks in advance!

Gustavo

Please also provide the following information in addition to your question:

- Operating System: Windows 10
- brms Version: 2.5.0