Hello,

I am interested in fitting growth models to some data with a variety of treatments/groups in the data. Right now my project has 15 groups (genotypes) but I am hoping to get some advice on general scalability. In the example below the `smallData.fit`

model will run and give good results but the `completeData.fit`

model will fail to initialize.

My initial values are confined to be positive by `rgamma`

since I am using lognormal priors, I have also tried `inits=0`

and setting inits as a list of lists containing values for a, b, and c for each chain (idea taken from here), but the model has consistently failed to initialize when I have more than 2 groups.

Is there a set of guidelines on how many groups a brms model should be able to handle? Has anyone had a similar situation and found a solution besides running separate models on subsets of the data (that option does work but it seems like a poor workaround)?

```
library(brms)
library(tidyverse)
## Setup simulated data
growthSim <- function(x,a,b,c){
a_r <- a+rnorm(1,mean = 0,sd=10)
b_r <- b+rnorm(1,mean=0,sd=2)
c_r <- c+rnorm(1,mean=0,sd=.035)
return(a_r*exp(-b_r*exp(-c_r*x)))}
x <- 1:25
smallDf <- rbind(do.call(rbind,lapply(letters[1:2], function(L) do.call(rbind, lapply(1:20,function(i) data.frame("sample"=paste0("sample_",i),"treatment"=L,"time"=x,"y"=growthSim(x,200-(5*which(letters==L)),13-(.2*which(letters==L)),.2+(0.001*which(letters==L))),stringsAsFactors = F))))))
completeDf <- rbind(do.call(rbind,lapply(letters[1:15], function(L) do.call(rbind, lapply(1:20,function(i) data.frame("sample"=paste0("sample_",i),"treatment"=L,"time"=x,"y"=growthSim(x,200-(5*which(letters==L)),13-(.2*which(letters==L)),.2+(0.001*which(letters==L))),stringsAsFactors = F))))))
completeDf%>%
ggplot(aes(time,y,group=interaction(treatment,sample)))+
geom_line(aes(color=treatment), show.legend=F)
## Define Priors
prior1 <- prior(lognormal(log(130), .25),nlpar = "a") +
prior(lognormal(log(12), .25), nlpar = "b") +
prior(lognormal(log(1.2), .25), nlpar = "c") +
prior(student_t(3,0,5), dpar="sigma") +
prior(gamma(2,0.1), class="nu")
## Run models
smallData.fit <- brm(bf(y ~ a*exp(-b*exp(-c*time)),
sigma~s(time,by=treatment),
a + b + c ~ 0+treatment,
autocor = ~arma(~time|sample:treatment,1,1),nl = TRUE),
family = student, prior = prior1, data = smallDf, iter = 2000,
cores = 4, chains = 4, backend = "cmdstanr",
control = list(adapt_delta = 0.999,max_treedepth = 20),
inits = function(){list(b_a=rgamma(2,1),b_b=rgamma(2,1),b_c=rgamma(2,1))})
completeData.fit <- brm(bf(y ~ a*exp(-b*exp(-c*time)),
sigma~s(time,by=treatment),
a + b + c ~ 0+treatment,
autocor = ~arma(~time|sample:treatment,1,1),nl = TRUE),
family = student, prior = prior1, data = completeDf, iter = 2000,
cores = 4, chains = 4, backend = "cmdstanr",
control = list(adapt_delta = 0.999,max_treedepth = 20),
inits = function(){list(b_a=rgamma(2,1),b_b=rgamma(2,1),b_c=rgamma(2,1))})
```

Error messages from trying to make `completeData.fit`

for all chains are:

```
Chain 4 Rejecting initial value:
Chain 4 Log probability evaluates to log(0), i.e. negative infinity.
Chain 4 Stan can't start sampling from this initial value.
Chain 4 Initialization between (-2, 2) failed after 100 attempts.
Chain 4 Try specifying initial values, reducing ranges of constrained values, or reparameterizing the model.
Warning: Chain 4 finished unexpectedly!
```

Also this is my first post so if I should provide more information or I have tagged this topic incorrectly please let me know! Thank you!