Hi.

I’m trying to fit a model that predicts whether an individual requires help to care for themselves in a longitudinal survey (binary outcome, repeated measure) based on a series of characteristics measured at baseline and others that are measured repeatedly. Our particular interest is in how 3 of those characteristics (all binary) interact — preferences, expression and race. I’m struggling with how to setup the data. Given that all of the data are ints, I initially set things up as an int arrays (2 dimensional for variables that change over time and 1 dimensional for variables that are only measured at baseline) and iterated across all patients (N) and survey wave (M) in my model. That worked when I put preferences, expression and race in as fixed effects in a logit model. When I’ve tried to put them in as partially pooled parameters, I’m struggling to figure out how to parameterize the data/specify the model. Now, when I loop I’m getting syntax errors:

- No matches for:

real[] * real

and

2.

expression is ill formed

error in ‘unkown file name’ at line 81, column 102

-------------------------------------------------

79: m*waveCoeff +

80: patientIntercept[n] +

81: alphaPreference[preference] * prefSD ) ;

^

82: }

I’m new to Stan and Bayesian modeling — this is my first attempt to fit something appproximating a model that I’d actually care about. Its pretty clear that I don’t really understand what I’m looping over or what the indices are referring to… It seems to me that there shouldn’t be a place where i’m multiplying a real[] by a real, unless I don’t understand what the data-baed indices (preference, expression , race) are doing and/or they’re not working the way I intuitively think they should be working.

I’m quite sure that there is a vector-based way that I could parameterize the model, but in order to make sure that I understood exactly what was happening, I was hoping to do this with loops…

Model is

```
data {
int<lower=0> N; //number of individuals
int<lower=0> M; //waves of data
int<lower=1, upper=2> preference[N]; //1 = non=aggressive, 2 = aggressive
int<lower=1, upper=2> expression[N]; //1 = no expression, 2 = expression
int<lower=1, upper=2> race[N]; //1 = white, 2 = black
int<lower=1, upper=8> prefExpressionRaceCat[N]; //3 way interaction...index 1 to 8 for each category
int<lower=1, upper=6> baselineAgeCat[N];
int<lower=0> physicalCapacity[N,M];
int<lower=0> clockDraw[N,M];
int<lower=0> memory[N,M];
int<lower=0> selfCareLimitationCount[N,M];
int<lower=0> anySelfCareActivityLimitation[N,M];
int<lower=0> dead[N,M];
}
parameters {
//intercepts for preference, expression, race and 3-way interaction groups
real alphaPreference[2];
real alphaExpression[2];
real alphaRace[2];
real alphaPrefExpressionRaceInteraction[8]; //3 way interaction between preferences, expression and race.
real meanPatientIntercept;
real<lower=0> sdPatientIntercept;
real<lower=0> prefSD;
real<lower=0> expressionSD;
real<lower=0> raceSD;
real<lower=0> prefExpressionRaceSD;
real physicalCapacityCoeff;
real clockDrawCoeff;
real memoryCoeff;
real ageCoeff;
real waveCoeff;
real patientIntercept[N];
real alpha;
}
model{
//fixed effects
physicalCapacityCoeff ~ normal(0,1);
clockDrawCoeff ~ normal(0, 1);
memoryCoeff ~ normal(0, 1);
ageCoeff ~ normal(0,1); //can probably put a non-centered prior on this as well
waveCoeff ~ normal(0, 1); //can probably put a non-normal prior on this...disabiltiy has to go up with waves
alpha ~ normal(0, 1);
//partially pooled effects
alphaPreference ~ normal(0, 1);
alphaExpression ~ normal(0, 1);
alphaRace ~ normal(0, 1);
alphaPrefExpressionRaceInteraction ~ normal(0, 1);
//description of partially pooled effect sizes
prefSD ~ normal(0, 1);
expressionSD ~ normal(0, 1);
raceSD ~ normal(0, 1);
prefExpressionRaceSD ~ normal(0, 1);
//random patient-level intercept
sdPatientIntercept ~ normal(0, 1);
meanPatientIntercept ~ normal(0, 1);
patientIntercept ~ normal(meanPatientIntercept, sdPatientIntercept);
for (n in 1:N) {
for (m in 1:M) { //note to self: come back and try to vectorize this...
anySelfCareActivityLimitation[n,m] ~ bernoulli_logit(alpha +
physicalCapacityCoeff *physicalCapacity[n,m] +
clockDrawCoeff * clockDraw[n,m] +
memoryCoeff * memory[n,m] +
ageCoeff * baselineAgeCat[n] +
m*waveCoeff +
patientIntercept[n] +
alphaPreference[preference] * prefSD +
alphaExpression[expression] * expressionSD +
alphaRace[race] * raceSD +
alphaPrefExpressionRaceInteraction[prefExpressionRaceCat] * prefExpressionRaceSD ) ;
}
}
}
generated quantities {
int y_ppc[N,M];
real phat_ppc = 0;
for (n in 1:N) {
for (m in 1:M) {
y_ppc[n,m] = bernoulli_logit_rng(alpha +
physicalCapacityCoeff *physicalCapacity[n,m] +
clockDrawCoeff * clockDraw[n,m] +
memoryCoeff * memory[n,m] +
ageCoeff * baselineAgeCat[n] +
m*waveCoeff +
patientIntercept[n] +
alphaPreference[preference] * prefSD +
alphaExpression[expression] * expressionSD +
alphaRace[race] * raceSD +
alphaPrefExpressionRaceInteraction[prefExpressionRaceCat] *prefExpressionRaceSD ) ;
phat_ppc = phat_ppc + y_ppc[n,m];
}
}
phat_ppc = phat_ppc / (N*M);
}
```