I’m trying to fit a shifted lognormal model to a large data set of response times. I’m having some difficulty with trade-offs between the ndt parameter and the scale parameter (mu): sometimes in one chain the ndt starts out out in an unreasonable part of the parameter space, and just stays there.

I’m seeing whether I can mitigate this with some reasonable priors, I’m less familiar with how to specify things in brms than I would be in stan or JAGS. I’m trying to specify a prior (which seems to work) and starting values. When I specify the starting values for `b_ndt`

as a function (see code below) the stan errors indicate problems with the starting values:

```
# SAMPLING FOR MODEL '48d47669f619bbb26676a523eb3d8c6e' NOW (CHAIN 1).
# Chain 1: Rejecting initial value:
# Chain 1: Error evaluating the log probability at the initial value.
# Chain 1: Exception: lognormal_lpdf: Random variable[1] is -163.201, but must be >= 0! (in 'model4b2c6ff13fd9_48d47669f619bbb26676a523eb3d8c6e' at line 118)
```

I don’t get this, given that Y is lognormally distributed and must be greater than 0, so I figure it must be some issue with the generation of starting values yielding incompatible values (?). I’d think that since everything is defined on the log scale this would work, but I guess I’m missing something.

Code follows.

```
## Read in the data
library(dplyr)
library(brms)
data_url = "https://www.dropbox.com/s/94ccglb6kd1ifm0/data.Rda?dl=1"
data_file = tempfile(fileext = ".Rda")
download.file(url = data_url,
destfile = data_file,
mode="wb")
load(data_file)
## brms
formula <- bf(LiftOffLatency ~ 0 + Intercept + CueDirection*SentenceDirection*duration + CueDirection*SentenceDirection*min_freq + (1|ptid) + (0+SentenceDirection|ItemNumber),
sigma ~ 0 + Intercept + (1|ptid),
ndt ~ 0 + Intercept + (1|ptid), family=shifted_lognormal())
priors <- c(
set_prior("normal(7,1)", class = "b", coef = "Intercept", dpar = "ndt")
)
fit = brm(formula = formula,
data = mono_data,
prior = priors,
chains = 1,
seed = 394,
control = list(
#adapt_delta = .95,
#max_treedepth = 15
), inits = function(){
list(b_ndt = array(runif(1,6,8)))
})
```

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

- Operating System: Windows and OSX
- brms Version: 2.13.3