Hello all,

I am interested in including an optional parameter in my model using an approach as described here

Optional Parameters/Data in Stan (martinmodrak.cz)

I’ve omitted most of the model as I think the main issue is my lack of understanding of Stan’s data types and function signatures.

In the simpler model, there is no data `y_age`

and the logical `incl_age`

is FALSE.

For the extended model, we have as many `y_age`

values as there are observations in the data and `incl_age`

is TRUE and the parameter `decay`

is a positive scalar.

My attempt at this leads to a Stan file similar to

```
data {
int<lower=1> n_obs;
// Other data ...
int<lower=0, upper=1> incl_age;
real<lower=0> y_age[incl_age ? n_obs : 0];
}
parameters {
// Other parameters ...
real<lower=0> decay[incl_age ? 1 : 0];
}
model {
// Other model statements ...
if (incl_age) target += decay * y_age;
}
```

This fails for both multiplication `*`

and elementwise multiplication `.*`

in the last line.

Is it possible to make these data and parameter optional and still increment a quantity by the scalar multiplied vector?

Would it be simpler, or perhaps more efficient, to simplify the process into separate Stan files?

Any feedback would be appreciated.

I’ve oversimplified this example to the point of confusion. Sorry.

- The Stan model was not compiling so it was failing independent of data.

That is, using the RStudio toolbar option “Check” (runs `rstan:::rstudio_stanc("inst/stan/dbm.stan")`

) was failing due to incompatible data types

`real array[] * real array[]`

, or previously
`real array[] * vector[]`

- Yes, the idea behind the model was to include a renewal process component in an existing model.

See my next reply for a better demonstration of what I was trying to achieve.

See the following for a Poisson regression with an adjustment relative to the “age” of each observation.

This is a no longer a typical Poisson process as the rate over time is not constant but the Stan code runs which is what I was struggling with.

```
data {
int<lower=1> n_obs;
int<lower=1> n_x;
array[n_obs] int y;
matrix[n_obs, n_x] x;
int<lower=0, upper=1> incl_age;
vector[incl_age ? n_obs : 0] y_age;
}
parameters {
real Intercept;
vector[n_x] regr;
real<lower=0> decay[incl_age ? 1 : 0];
}
transformed parameters {
// Expecation adjustment
vector[n_obs] adjust = rep_vector(0.0, n_obs);
adjust += Intercept;
if (incl_age) adjust += decay[1] * y_age;
}
model {
// Prior Information
Intercept ~ normal(1, 100);
regr ~ normal(1, 100);
if (incl_age) decay ~ gamma(1, 0.1);
// Log likelihood
target += poisson_log_glm_lpmf(y | x, adjust, regr);
}
```

The subsetting of the 1-length array (i.e. `decay[1]`

) doesn’t look natural but should be fine as it is only used inside the conditional and we know it’s length will always be 1

I see you’ve marked your reply as the solution. Is this resolved? If not, could you post the full code that’s not running?