I’m fitting a model of population counts over time in three different locations with some random effects structure. The model fits fine using brms but when I plot the conditional effects the lines of the resultant graph don’t pass through any of the data points. The posterior predictive plot looks OK so I’m wondering if this is a consequence of the choice of priors?

Here’s the model:

```
# specify the priors
newprior_hv <-
c(
set_prior("normal(0, .2)", class = "b", coef = "Intercept"),
set_prior("normal(0, .2)", class = "b", coef = "NPRuaha"),
set_prior("normal(0, .2)", class = "b", coef = "NPSelous")
)
#' fit the model
nbglm_norm_prior_tight_hv <-
brm(
HV ~ 0 + Intercept + # this allows control of prior on the intercept
ndate * NP + Season + CarcassPres +
(1 | NP / StandardTransect) +
offset(log(Tlength)),
family = negbinomial,
data = mydata,
chains = 4,
control = list(adapt_delta = 0.999, max_treedepth = 15),
prior = newprior_hv,
save_pars = save_pars(all = TRUE)
)
#' check the models
plot(nbglm_norm_prior_tight_hv)
plot(conditional_effects(nbglm_norm_prior_tight_hv, prob = 0), ask = FALSE, points = T)
```

Here’s the data:

```
mydata <- structure(list(HV = c(0, 1, 21, 2, 1, 5, 8, 3, 1, 7, 2, 0, 1,
0, 2, 0, 0, 0, 0, 12, 2, 0, 4, 0, 3, 5, 2, 0, 0, 3, 7, 0, 6,
8, 8, 0, 0, 2, 2, 3, 2, 3, 3, 0, 0, 0, 0, 4, 1, 1, 0, 8, 1, 0,
3, 11, 0, 2, 1, 0, 5, 0, 4, 0, 2, 0, 5, 4, 2, 9, 5, 0, 0, 16,
7, 2, 1, 0, 0, 2, 0, 1, 2, 5, 1, 7, 0, 1, 0, 2, 0, 6, 16, 0,
8, 4, 1, 0, 7, 6, 6, 0, 1, 1, 3, 2, 4, 2), ndate = c(0, 0, 0,
0, 0, 14, 14, 14, 14, 14, 14, 19, 19, 19, 19, 20, 20, 25, 25,
25, 25, 25, 25, 32, 32, 33, 33, 33, 33, 33, 38, 38, 38, 38, 38,
38, 39, 39, 39, 39, 39, 41, 41, 42, 42, 42, 42, 43, 43, 43, 44,
46, 46, 46, 46, 51, 51, 51, 51, 51, 51, 54, 54, 54, 55, 56, 56,
56, 58, 58, 61, 61, 61, 61, 61, 63, 63, 66, 66, 67, 67, 67, 68,
68, 68, 68, 68, 71, 72, 73, 78, 78, 79, 79, 89, 89, 89, 90, 90,
91, 91, 91, 96, 96, 96, 96, 97, 97), nyear = c(2013, 2013, 2013,
2013, 2013, 2014, 2014, 2014, 2014, 2014, 2014, 2015, 2015, 2015,
2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2016, 2016,
2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016,
2016, 2016, 2016, 2016, 2016, 2017, 2017, 2017, 2017, 2017, 2017,
2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017,
2017, 2017, 2017, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018,
2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2020,
2020, 2020, 2020, 2021, 2021, 2021, 2021, 2021, 2021, 2021, 2021,
2021, 2021, 2021, 2021, 2021, 2021), NP = structure(c(1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 3L, 3L, 2L,
2L, 2L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 3L, 3L, 3L,
2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 3L, 3L, 2L, 2L, 2L, 1L,
1L, 3L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 1L), .Label = c("Katavi",
"Ruaha", "Selous"), class = "factor"), Season = c("Dry", "Dry",
"Dry", "Dry", "Dry", "Dry", "Dry", "Dry", "Dry", "Dry", "Dry",
"Wet", "Wet", "Wet", "Wet", "Wet", "Wet", "Dry", "Dry", "Dry",
"Dry", "Dry", "Dry", "Wet", "Wet", "Wet", "Wet", "Wet", "Wet",
"Wet", "Wet", "Dry", "Dry", "Dry", "Dry", "Dry", "Dry", "Dry",
"Dry", "Dry", "Dry", "Wet", "Wet", "Wet", "Wet", "Wet", "Wet",
"Wet", "Wet", "Wet", "Wet", "Dry", "Dry", "Dry", "Dry", "Dry",
"Dry", "Dry", "Dry", "Dry", "Dry", "Wet", "Wet", "Wet", "Wet",
"Wet", "Wet", "Wet", "Dry", "Dry", "Dry", "Wet", "Dry", "Dry",
"Dry", "Dry", "Dry", "Wet", "Wet", "Wet", "Wet", "Wet", "Wet",
"Wet", "Wet", "Wet", "Wet", "Dry", "Dry", "Dry", "Wet", "Wet",
"Wet", "Wet", "Wet", "Wet", "Wet", "Wet", "Wet", "Wet", "Wet",
"Wet", "Dry", "Dry", "Dry", "Dry", "Dry", "Dry"), CarcassPres = structure(c(1L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L), .Label = c("0",
"1"), class = "factor"), StandardTransect = structure(c(4L, 3L,
1L, 5L, 7L, 5L, 1L, 8L, 6L, 4L, 3L, 8L, 5L, 6L, 1L, 4L, 3L, 4L,
3L, 5L, 1L, 8L, 6L, 8L, 6L, 5L, 1L, 8L, 6L, 1L, 5L, 5L, 1L, 6L,
4L, 3L, 8L, 6L, 5L, 1L, 8L, 4L, 3L, 5L, 1L, 8L, 6L, 5L, 1L, 6L,
8L, 5L, 1L, 8L, 6L, 4L, 3L, 5L, 1L, 8L, 6L, 3L, 4L, 10L, 2L,
1L, 5L, 6L, 2L, 10L, 5L, 8L, 8L, 1L, 6L, 3L, 4L, 4L, 3L, 9L,
2L, 10L, 5L, 1L, 7L, 5L, 7L, 2L, 10L, 9L, 4L, 3L, 10L, 2L, 5L,
1L, 7L, 4L, 3L, 2L, 10L, 9L, 5L, 7L, 1L, 2L, 9L, 4L), .Label = c("Jongomero",
"Kidai", "LakeChada", "LakeKatavi", "Lunda", "Magangwe", "Mbagi-Mdonya",
"Mpululu", "Msolwa", "Mtemere"), class = "factor"), Tlength = c(35.2,
86.7, 93, 75, 27.2, 74.4, 93, 10.3, 45.8, 35.2, 78.2, 10.3, 71,
45.8, 93, 35.2, 63.9, 35.2, 77.9, 86.6, 93, 10.3, 45.8, 10.3,
45.8, 68.9, 93, 10.3, 45.8, 93, 86.7, 90.5, 93, 45.8, 35.2, 81.6,
10.3, 45.8, 88.2, 93, 10.3, 35.2, 64.6, 82.3, 93, 10.3, 45.8,
77.9, 93, 45.8, 10.3, 90.3, 93, 10.3, 45.8, 35.2, 77.4, 87.5,
93, 10.3, 45.8, 66, 35.2, 71.2, 85.7, 93, 87.5, 45.8, 85.5, 69.6,
97.8, 10.3, 10.3, 93, 45.8, 86.6, 35.2, 35.2, 71.9, 77.9, 88.5,
80, 85.2, 93, 56.1, 85.5, 56.1, 97.6, 81.8, 79.7, 35.2, 71.1,
81.9, 53.8, 86.5, 68.7, 60.7, 78.7, 56.6, 66.9, 71.8, 79.2, 82.6,
71.8, 92.4, 85.6, 78.5, 77.3)), row.names = c(NA, -108L), class = "data.frame")
```