I’m trying to estimate what I think would be called a latent class linear mixed model (or possibly a growth mixture model) and having trouble getting it to converge.

I have repeated measures of a binary outcome – past-six-month heroin use – from 2,923 participants totaling 62,065 observations. The goal is to describe common life-course trajectories of heroin use: in the model there are K latent trajectory classes, and within each class the conditional log odds of use is modeled as a cubic function of age with a multilevel intercept (level 1 --> observation; level 2 --> person). There is what I think is a modestly informative student-t prior on the grand model intercept in each class, to break the symmetry between classes – the priors are supposed to correspond to a weak prior belief that the probability of use in one class will be 35% and 65% for the other class at age 45 (these are plausible values in this cohort).

Here is the BRMS code used to generate the 2-class model (chains = 0 because I only use BRMS to help with the code, I run the models in CmdStan because that works better on the computing cluster I’m using):

```
# 2 Class Model
mix2 <- mixture(bernoulli,bernoulli)
pri2 <- c(
prior(
student_t(3,-.619,5),Intercept,dpar = mu1 #35%
),
prior(
student_t(3,.619,5),Intercept,dpar = mu2 # 65%
)
)
mcmc.2class <- brm(heroin ~ age.std + age2.std + age3.std + female + black + cohort + visit.era + (1|newid),
data = alive.complete,
family = mix2,
prior = pri2,
chains = 0
)
```

Here is the actual Stan code for that model:

```
// generated with brms 2.5.0
functions {
}
data {
int<lower=1> N; // total number of observations
int Y[N]; // response variable
int<lower=1> K_mu1; // number of population-level effects
matrix[N, K_mu1] X_mu1; // population-level design matrix
int<lower=1> K_mu2; // number of population-level effects
matrix[N, K_mu2] X_mu2; // population-level design matrix
vector[2] con_theta; // prior concentration
// data for group-level effects of ID 1
int<lower=1> J_1[N];
int<lower=1> N_1;
int<lower=1> M_1;
vector[N] Z_1_mu1_1;
// data for group-level effects of ID 2
int<lower=1> J_2[N];
int<lower=1> N_2;
int<lower=1> M_2;
vector[N] Z_2_mu2_1;
int prior_only; // should the likelihood be ignored?
}
transformed data {
int Kc_mu1 = K_mu1 - 1;
matrix[N, K_mu1 - 1] Xc_mu1; // centered version of X_mu1
vector[K_mu1 - 1] means_X_mu1; // column means of X_mu1 before centering
int Kc_mu2 = K_mu2 - 1;
matrix[N, K_mu2 - 1] Xc_mu2; // centered version of X_mu2
vector[K_mu2 - 1] means_X_mu2; // column means of X_mu2 before centering
for (i in 2:K_mu1) {
means_X_mu1[i - 1] = mean(X_mu1[, i]);
Xc_mu1[, i - 1] = X_mu1[, i] - means_X_mu1[i - 1];
}
for (i in 2:K_mu2) {
means_X_mu2[i - 1] = mean(X_mu2[, i]);
Xc_mu2[, i - 1] = X_mu2[, i] - means_X_mu2[i - 1];
}
}
parameters {
vector[Kc_mu1] b_mu1; // population-level effects
vector[Kc_mu2] b_mu2; // population-level effects
simplex[2] theta; // mixing proportions
ordered[2] ordered_Intercept; // to identify mixtures
vector<lower=0>[M_1] sd_1; // group-level standard deviations
vector[N_1] z_1[M_1]; // unscaled group-level effects
vector<lower=0>[M_2] sd_2; // group-level standard deviations
vector[N_2] z_2[M_2]; // unscaled group-level effects
}
transformed parameters {
// identify mixtures via ordering of the intercepts
real temp_mu1_Intercept = ordered_Intercept[1];
// identify mixtures via ordering of the intercepts
real temp_mu2_Intercept = ordered_Intercept[2];
// mixing proportions
real<lower=0,upper=1> theta1 = theta[1];
real<lower=0,upper=1> theta2 = theta[2];
// group-level effects
vector[N_1] r_1_mu1_1 = sd_1[1] * (z_1[1]);
// group-level effects
vector[N_2] r_2_mu2_1 = sd_2[1] * (z_2[1]);
}
model {
vector[N] mu1 = temp_mu1_Intercept + Xc_mu1 * b_mu1;
vector[N] mu2 = temp_mu2_Intercept + Xc_mu2 * b_mu2;
for (n in 1:N) {
mu1[n] += r_1_mu1_1[J_1[n]] * Z_1_mu1_1[n];
mu2[n] += r_2_mu2_1[J_2[n]] * Z_2_mu2_1[n];
}
// priors including all constants
target += student_t_lpdf(temp_mu1_Intercept | 3, -0.619, 5);
target += student_t_lpdf(temp_mu2_Intercept | 3, 0.619, 5);
target += dirichlet_lpdf(theta | con_theta);
target += student_t_lpdf(sd_1 | 3, 0, 10)
- 1 * student_t_lccdf(0 | 3, 0, 10);
target += normal_lpdf(z_1[1] | 0, 1);
target += student_t_lpdf(sd_2 | 3, 0, 10)
- 1 * student_t_lccdf(0 | 3, 0, 10);
target += normal_lpdf(z_2[1] | 0, 1);
// likelihood including all constants
if (!prior_only) {
for (n in 1:N) {
real ps[2];
ps[1] = log(theta1) + bernoulli_logit_lpmf(Y[n] | mu1[n]);
ps[2] = log(theta2) + bernoulli_logit_lpmf(Y[n] | mu2[n]);
target += log_sum_exp(ps);
}
}
}
generated quantities {
// actual population-level intercept
real b_mu1_Intercept = temp_mu1_Intercept - dot_product(means_X_mu1, b_mu1);
// actual population-level intercept
real b_mu2_Intercept = temp_mu2_Intercept - dot_product(means_X_mu2, b_mu2);
}
```

Here are the trace plots of the grand intercepts (and a few other covariates) after running 1,000 warmups and 1,000 iterations for three chains. No convergence, and seemingly pretty high autocorrelation.

Any suggestions about how to get the model to converge? Some thoughts:

- Am I even specifying the model correctly?
- I could make the intercept priors more informative. I’m reluctant to do this, because I really don’t have strong prior beliefs. But maybe there is a way to do this in a way that won’t bias the model?
- I could run for more warmup or more iterations. But nothing about the traceplot makes me think even a very long run time will get me there.
- I could thin the chain, but again, that doesn’t look promising, and also I gather that for HMC this is generally not that helpful.
- I could allow for multilevel age slopes (right now just the intercept is multilevel). This would add many more parameters of course, but I gather that sometimes a more complex model actually converges faster because it fits the data better.
- I could go back to the drawing board and consider whether this is really an appropriate model for the data.

Thanks