Hierarchical Mixture Model in Stan - stan

Question

I'm trying to implement a hierarchical mixture model in Stan that describes how performance on a task changes over time. In the model (see code below), there are three lower level parameters that are assumed to be drawn from a mixture of two normals (dperf_int, dperf_sd, and sf). To implement these, I've just assumed that the prior on these lower level parameters is a mixture. There are also two parameters (perf1_int and perf1_sd) that are only estimated at the group level. For these, I've assumed that the observation itself is drawn from a mixture of normals.
data {
int<lower=0> Nsubj; //Number of subjects (60)
int<lower=0> Nobs; //Number of observations per subject (10)
matrix[Nsubj,Nobs] perf; //Matrix of performance observations
}
parameters {
vector<lower=0,upper=1>[Nsubj] mixing_prop; // mixing proportion for each subject
ordered[2] perf1_int; //mean performance at time 1 (estimated at group level only)
vector<lower=0>[2] perf1_sd; //sd of performance at time 1 (estimated at group level only)
ordered[2] dperf_int_mean; //mean of performance change intercept for each mixture
vector<lower=0>[2] dperf_int_sd; //sd of performance change intercept for each mixture
vector<lower=0>[2] dperf_sd_mean; //mean of performance change sd
vector<lower=0>[2] dperf_sd_sd; //sd of performance change sd
vector[2] sf_mean; //mean self-feedback for each mixture
vector<lower=0>[2] sf_sd; //sd of self-feedback for each mixture
//subject level parameters
vector[Nsubj] dperf_int;
vector<lower=0>[Nsubj] dperf_sd;
vector[Nsubj] sf;
}
model {
real perf_change;
for(i in 1:Nsubj){
vector[4] increments;
increments[1]=log_mix(mixing_prop[i],
normal_lpdf(dperf_int[i] | dperf_int_mean[1], dperf_int_sd[1]),
normal_lpdf(dperf_int[i] | dperf_int_mean[2], dperf_int_sd[2]));
increments[2]=log_mix(mixing_prop[i],
normal_lpdf(dperf_sd[i] | dperf_sd_mean[1], dperf_sd_sd[1]),
normal_lpdf(dperf_sd[i] | dperf_sd_mean[2], dperf_sd_sd[2]));
increments[3]=log_mix(mixing_prop[i],
normal_lpdf(sf[i] | sf_mean[1], sf_sd[1]),
normal_lpdf(sf[i] | sf_mean[2], sf_sd[2]));
increments[4]=log_mix(mixing_prop[i],
normal_lpdf(perf[i,1] | perf1_int[1], perf1_sd[1]),
normal_lpdf(perf[i,1] | perf1_int[2], perf1_sd[2]));
target+= log_sum_exp(increments);
for(j in 2:Nobs){
perf_change = dperf_int[i] + perf[i,j-1]*sf[i];
perf[i,j] ~ normal( perf[i,j-1] + perf_change, dperf_sd[i]);
}
}
}
I'm pretty sure I've implemented the mixture of the group-level parameters correctly, as I've just done what was in the Stan manual. However, I wanted to check that I've done the hierarchical mixture components of the model correctly. The model is quite inefficient. I've run 4 chains at 4000 iterations and many of the parameters have < 10 n_eff. So I suspect I'm missing something.
Here is the code used to run the model:
library(tidyverse)
library(rstan)
#load data
load(file="wide_data.RData")
#prepare data for stan
model_data = list(
Nsubj = dim(wide_data)[1],
Nobs = 10,
perf = as.matrix(select(wide_data ,perf1:perf10))
)
fit = stan(file="stan_univariate_hierarchical_mixture.stan",
data=model_data,cores=4,
thin=10,
warmup=1000,
chains=4,
iter=4000)
...and the data is here: https://www.dropbox.com/s/eqzw1lou6uba8i3/wide_data.RData?dl=0
Any help would be much appreciated!