I am attempting to apply a Bayesian mixed model to estimate the impact of temperature on a home’s energy consumption.

```
priors <- c(set_prior("lognormal(0.01494647, 0.00937701)", class = "b", coef = "Temp"))
ins_month <- brm(use ~ Temp + (1+Temp|Month.f),
data = dpa.ins.home, family = gaussian(link="log"), warmup = 100, iter = 200, chains = 2, inits = "random", control = list(adapt_delta = .95, max_treedepth = 12),
prior = priors, cores = 2, sample_prior = TRUE))
```

While I am comfortable with mixed-effects regression formulation, I am struggling with the details of the **brms** formula syntax specifically the **priors** and **families** . With the below information am I using priors and families correctly?

energy_use has a beta distribution

```
> descdist(ACH_test$use)
summary statistics
----------
min: 0.1473333 max: 10.4126
median: 0.6733333
mean: 1.349991
estimated sd: 1.475063
estimated skewness: 1.857041
estimated kurtosis: 6.252937
```

The prior for Temp is closes to a lognormal distribution

```
> descdist(temp$T.Impact)
summary statistics
------
min: 0 max: 0.1714414
median: 0.01265037
mean: 0.01494647
estimated sd: 0.009377701
estimated skewness: 2.071458
estimated kurtosis: 12.71367
```

- Operating System: Windows 10
- brms Version: