I have some data from a behavioural experiment with multiple potential choice options that I want to fit with a non-linear model. To ensure I am setting the appropriate priors, I’m attempting to use `get_prior`

to get a handle on which parameters need priors. However, this seems to give me the error message, “Error: The parameter ‘name’ is not a valid distributional or non-linear parameter. Did you forget to set ‘nl = TRUE’?”, and I’m not sure why.

Here is a reproducible example of the issue:

```
tmp <- data.frame(X = sample(c("condition1", "condition2"), size=1000, replace=TRUE))
tmp$weight1 <- ifelse(tmp$X=="condition1",
rnorm(1000, 1, 1)+rnorm(1000, 0.5, 1),
rnorm(1000, 1, 1))
tmp$weight2 <- ifelse(tmp$X=="condition1",
rnorm(1000, 1, 1),
rnorm(1000, 1, 1)+rnorm(1000, 0.5, 1))
tmp$weight3 <- rnorm(1000, 0.5, 1)
tmp$response <- ""
for (i in 1:1000){
tmp$response[i] <- sample(c("response1", "response2", "response3"),
size=1,
prob=c(exp(tmp$weight1[i])/
(exp(tmp$weight1[i]) + exp(tmp$weight2[i]) + exp(tmp$weight3[i])),
exp(tmp$weight2[i])/
(exp(tmp$weight1[i]) + exp(tmp$weight2[i]) + exp(tmp$weight3[i])),
exp(tmp$weight3[i])/
(exp(tmp$weight1[i]) + exp(tmp$weight2[i]) + exp(tmp$weight3[i]))))
}
testFormula <- bf(response ~ x1 + x2,
muresponse2 ~ x1 + x3,
muresponse3 ~ x4,
x1 ~ 1,
x2 ~ X,
x3 ~ X,
x4 ~ 1,
nl=TRUE,
family = categorical(link="logit"))
get_prior(testFormula, data=tmp)
> Error: The parameter 'x1' is not a valid distributional or non-linear parameter. Did you forget to set 'nl = TRUE'?
```

One variation I tried was specifying the different responses within `nlf()`

(as per the post here). This gives the same error, but concerning a different parameter:

```
testFormula <- bf(response ~ x1 + x2,
nlf(muresponse2 ~ x1 + x3),
nlf(muresponse3 ~ x4),
x1 ~ 1,
x2 ~ X,
x3 ~ X,
x4 ~ 1,
nl=TRUE,
family = categorical(link="logit"))
get_prior(testFormula, data=tmp)
> Error: The parameter 'x2' is not a valid distributional or non-linear parameter. Did you forget to set 'nl = TRUE'?
```

However, if I comment out the non-linear formula for the third response (and the associated x4 parameter), `get_prior`

works:

```
testFormula <- bf(response ~ x1 + x2,
nlf(muresponse2 ~ x1 + x3),
#nlf(muresponse3 ~ x4),
x1 ~ 1,
x2 ~ X,
x3 ~ X,
#x4 ~ 1,
nl=TRUE,
family = categorical(link="logit"))
get_prior(testFormula, data=tmp)
> prior class coef group resp dpar nlpar lb ub source
(flat) b default
(flat) b x1 default
(flat) b Intercept x1 (vectorized)
(flat) b x2 default
(flat) b Intercept x2 (vectorized)
(flat) b Xcondition2 x2 (vectorized)
(flat) b x3 default
(flat) b Intercept x3 (vectorized)
(flat) b Xcondition2 x3 (vectorized)
```

This is also the case if I comment out the formula for the second response (and associated x3 parameter):

```
testFormula <- bf(response ~ x1 + x2,
#nlf(muresponse2 ~ x1 + x3),
nlf(muresponse3 ~ x4),
x1 ~ 1,
x2 ~ X,
#x3 ~ X,
x4 ~ 1,
nl=TRUE,
family = categorical(link="logit"))
get_prior(testFormula, data=tmp)
> prior class coef group resp dpar nlpar lb ub source
(flat) b default
(flat) b x1 default
(flat) b Intercept x1 (vectorized)
(flat) b x2 default
(flat) b Intercept x2 (vectorized)
(flat) b Xcondition2 x2 (vectorized)
(flat) b x4 default
(flat) b Intercept x4 (vectorized)
```

I am clearly doing something wrong, but I can’t see what it is, so would appreciate any assistance a set of fresh eyes could provide.