I apologise as I am very new to this package and I really appreciate any help I can get.

I have a brms model with a categorical response variable (`Species`

) with the following formula, running a multinomial logistics regression (I believe). `Species ~ Density_1 + Density_2 + Canopy_Height + Soil_texture + pH + (1 | Fragment)`

.

All the explanatory variables are continuous and represent different habitat characteristics for six different plant species. I want to see which variables are correlated with the presence of which species. I need now to conduct some post hoc analysis for this model since the first species was used as the intercept and I am unable to see the effect of the habitat variables on this first species. I have tried using `emmeans `

and `bayestestR `

but neither seem compatible with the categorical brmsfit model object. Any suggestions for what I can do ?

Below is my model code:

```
model1_07<- brm(Species ~
Density_1 +
Density_2 +
Canopy_Height +
Soil_texture +
pH
+ (1 | Fragment)
, data = df_scaled_use,
family = categorical(),
iter = 10000, #may need to have upwards of 10000
# burn =
thin=1,
save_pars = save_pars(all = TRUE))
```

Here is my model output if that is helpful:

```
Family: categorical
Links: muCprestonianus = logit; muCpsammophilus = logit; muCsaintelucei = logit; muDbrevicaulis = logit; muDscottiana = logit
Formula: Species ~ Density_1 + Density_2 + Canopy_Height + Soil_texture + pH + (1 | Fragment)
Data: df_scaled_use (Number of observations: 120)
Draws: 4 chains, each with iter = 10000; warmup = 5000; thin = 1;
total post-warmup draws = 20000
Multilevel Hyperparameters:
~Fragment (Number of levels: 5)
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sd(muCprestonianus_Intercept) 4.83 2.50 1.70 11.16 1.00 10823 11896
sd(muCpsammophilus_Intercept) 1.17 1.05 0.04 3.88 1.00 7864 9312
sd(muCsaintelucei_Intercept) 1.01 0.88 0.04 3.30 1.00 6633 5801
sd(muDbrevicaulis_Intercept) 4.80 2.91 1.61 12.87 1.00 2317 864
sd(muDscottiana_Intercept) 1.46 0.95 0.13 3.78 1.00 6741 6506
Regression Coefficients:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
muCprestonianus_Intercept -1.24 1.91 -5.34 2.26 1.00 12108 11102
muCpsammophilus_Intercept -0.25 0.93 -2.22 1.44 1.00 8064 8688
muCsaintelucei_Intercept -0.17 0.85 -1.99 1.45 1.00 8935 5006
muDbrevicaulis_Intercept -2.78 2.36 -8.33 1.06 1.00 10170 8498
muDscottiana_Intercept 0.16 0.94 -1.70 2.05 1.00 11847 12408
muCprestonianus_Density_1 -1.75 1.10 -4.12 0.22 1.00 4875 1057
muCprestonianus_Density_2 0.72 0.85 -0.76 2.58 1.00 11069 12796
muCprestonianus_Canopy_Height -0.45 0.57 -1.55 0.71 1.00 15702 12974
muCprestonianus_Soil_texture -0.64 0.75 -2.16 0.78 1.00 6655 4310
muCprestonianus_pH 1.95 0.76 0.55 3.57 1.00 12281 11766
muCpsammophilus_Density_1 -1.91 0.77 -3.50 -0.46 1.00 10998 12544
muCpsammophilus_Density_2 -2.69 0.81 -4.33 -1.21 1.00 12036 14095
muCpsammophilus_Canopy_Height -1.29 0.58 -2.47 -0.22 1.00 12979 14650
muCpsammophilus_Soil_texture 0.24 0.57 -0.88 1.38 1.00 9225 11769
muCpsammophilus_pH 0.28 0.66 -1.01 1.61 1.00 11301 11847
muCsaintelucei_Density_1 0.92 0.60 -0.22 2.13 1.00 11401 12351
muCsaintelucei_Density_2 -1.93 0.64 -3.27 -0.76 1.00 10004 11519
muCsaintelucei_Canopy_Height -1.03 0.55 -2.14 0.03 1.00 12773 14404
muCsaintelucei_Soil_texture -1.77 0.61 -3.06 -0.65 1.00 11457 12137
muCsaintelucei_pH 0.37 0.59 -0.78 1.54 1.00 13250 13415
muDbrevicaulis_Density_1 -1.60 0.74 -3.14 -0.22 1.00 10458 11812
muDbrevicaulis_Density_2 -1.89 0.74 -3.42 -0.54 1.00 11121 11950
muDbrevicaulis_Canopy_Height -0.86 0.57 -1.97 0.26 1.00 13287 14843
muDbrevicaulis_Soil_texture 1.30 0.64 0.09 2.61 1.00 10737 12441
muDbrevicaulis_pH 1.68 0.71 0.35 3.16 1.00 11590 10872
muDscottiana_Density_1 -0.85 0.70 -2.28 0.48 1.00 8326 12671
muDscottiana_Density_2 -3.00 0.79 -4.67 -1.58 1.00 10508 8612
muDscottiana_Canopy_Height -2.72 0.70 -4.16 -1.41 1.00 12085 13191
muDscottiana_Soil_texture 0.57 0.60 -0.58 1.76 1.00 9218 11485
muDscottiana_pH 1.31 0.64 0.07 2.59 1.00 9169 5931
```

Many many thanks!

I have tried emmeans and bayestestR for doing a post hoc test. I have also been using `conditional_effects()`

to get the effects of my predictors.

Here is simulated data that works in my model. I scaled my original data so this is also scaled.

```
sim_data <- data.frame(
Species = sample(c("D. scottiana", "C. psammophilus", "D. brevicaulis", "C. saintelucei", "C. prestonianus", "B. madagascariensis"), 100, replace = TRUE),
Fragment = sample(paste0("S", 6:10), 100, replace = TRUE),
Density_1 = rnorm(100, mean = 0, sd = 1),
Density_2 = rnorm(100, mean = 0, sd = 1),
Canopy_Height = rnorm(100, mean = 0, sd = 1),
Soil_texture = rnorm(100, mean = 0, sd = 1),
pH = rnorm(100, mean = 0, sd = 1)
)
```