# Base type mismatch, what is wrong?

**URL:** <https://discourse.mc-stan.org/t/base-type-mismatch-what-is-wrong/2343>\
**Category:** Modeling\
**Created:** [October 26, 2017, 10:08pm UTC](https://discourse.mc-stan.org/t/base-type-mismatch-what-is-wrong/2343 "2017-10-26T22:08:28Z")\
**Posts on this page:** 8\
**Page:** 1

<div class="post-metadata">

**Author:** ![pfan2017](https://avatars.discourse-cdn.com/v4/letter/p/e8c25b/32.png) [@pfan2017](https://discourse.mc-stan.org/u/pfan2017)\
**Post date:** [October 26, 2017, 10:08pm UTC](https://discourse.mc-stan.org/t/base-type-mismatch-what-is-wrong/2343/1 "2017-10-26T22:08:28Z")

</div>

I am trying to run this stan file (without any input yet):

`data {  
int\<lower=0\> N; // number of data points  
int\<lower=0\> J; // number of random effects  
int\<lower=1,upper=J\> groups[N]; //group assignment  
matrix[N, J] X; // design matrix  
real y[N];  
}

parameters {  
vector[J] a; //intercepts  
real b; // random effects  
real mu\_a; // mean of random effects  
real\<lower=0,upper=100\> sigma\_a; //random effects sd  
real\<lower=0,upper=100\> sigma\_y; // population sd  
}  
transformed parameters {  
vector[N] y\_hat;  
for (n in 1:N)  
y\_hat[n] = a[groups[n]] + X[n] \* b;  
}  
model {  
sigma\_a ~ uniform(0, 100);  
a ~ normal (mu\_a, sigma\_a);  
b ~ normal (0, 1);  
sigma\_y ~ uniform(0, 100);  
y ~ normal(y\_hat, sigma\_y);  
}  
`  
I received the message:

`SYNTAX ERROR, MESSAGE(S) FROM PARSER: base type mismatch in assignment; variable name = y_hat, type = real; right-hand side type=row vector error in 'model54c74905f38d_random_intercepts' at line 18, column 15`

I tried declaring the `y_hat` variable with `row vector[N] y_hat` but same error came up. Why does it think `y_hat` is `real` when it is specified as `vector[N]`?

---

<div class="post-metadata">

**Author:** ![bbbales2](https://yyz2.discourse-cdn.com/flex030/user_avatar/discourse.mc-stan.org/bbbales2/32/77_2.png) [@bbbales2](https://discourse.mc-stan.org/u/bbbales2)\
**Post date:** [October 26, 2017, 10:51pm UTC](https://discourse.mc-stan.org/t/base-type-mismatch-what-is-wrong/2343/2 "2017-10-26T22:51:25Z")

</div>

In this expression:

```stan
y_hat[n] = a[groups[n]] + X[n] * b;

```

`y_hat` is a vector, so `y_hat[n]` is a real.

The issue is that `X[n]` is a row\_vector, `b` is a scalar, and `a[groups[n]]` is a scalar, so `a[groups[n]] + X[n] * b` all together comes out a row\_vector, and you can’t assign that to a real (`y_hat[n]`).

I think you want to have b be a row\_vector of length J (so J b coefficients).

```
vector[J] b;

```

---

<div class="post-metadata">

**Author:** ![pfan2017](https://avatars.discourse-cdn.com/v4/letter/p/e8c25b/32.png) [@pfan2017](https://discourse.mc-stan.org/u/pfan2017)\
**Post date:** [October 26, 2017, 11:55pm UTC](https://discourse.mc-stan.org/t/base-type-mismatch-what-is-wrong/2343/3 "2017-10-26T23:55:50Z")

</div>

Thank you!!

I ran the model and got the following error message:

`Initialization between (-2, 2) failed after 100 attempts. Try specifying initial values, reducing ranges of constrained values, or reparameterizing the model.`

How can I approach solving this issue? (Where can I specify initial values?)  
As you can see, I am trying to fit a varying intercept model.  
The input design matrix has rows representing a document and columns are word dummy variables.

I have tried several different models with different parameterization, but all ended up in the same error as above. I’m running out of ideas of how to “reparameterizing” the model. But maybe there’s something about my data that I’m not addressing.

Any suggestions is appreciated!

---

<div class="post-metadata">

**Author:** ![bbbales2](https://yyz2.discourse-cdn.com/flex030/user_avatar/discourse.mc-stan.org/bbbales2/32/77_2.png) [@bbbales2](https://discourse.mc-stan.org/u/bbbales2)\
**Post date:** [October 27, 2017, 12:03am UTC](https://discourse.mc-stan.org/t/base-type-mismatch-what-is-wrong/2343/4 "2017-10-27T00:03:57Z")

</div>

Can you post the new model and maybe a copy of the data that you’re passing in?

This sorta error happens when constraints are violated or there are uninitialized variables in the code or NaNs in the data (so the likelihood cannot be evaluated).

---

<div class="post-metadata">

**Author:** ![pfan2017](https://avatars.discourse-cdn.com/v4/letter/p/e8c25b/32.png) [@pfan2017](https://discourse.mc-stan.org/u/pfan2017)\
**Post date:** [October 29, 2017, 1:36am UTC](https://discourse.mc-stan.org/t/base-type-mismatch-what-is-wrong/2343/5 "2017-10-29T01:36:42Z")

</div>

Really appreciate the help, Ben!  
I made the change from above and fixed the outcome variable, so the previous model works.  
Now I am trying to get this model working:

```
  data {
    int<lower=1> N; //number of data points, 692
    real y[N]; //y, 692
    int<lower=1> J; //number of words, 665
    matrix[N, J] X; //692 x 665
    int<lower=1, upper=J> groups[N]; //word id
  }

   parameters {
     real beta1; // fixed intercept
     vector[J] beta2; // fixed slope
     vector[J] u; //random intercepts
     real<lower=0> sigma_y; //population sd
     real<lower=0> sigma_a; //random effects sd
   }

   model {
     real mu;
     //real mu;
     //priors
     u ~ normal(0, sigma_a); //word random effects
     // likelihood
     for (n in 1:N){
       mu = beta1 + u[groups[n]] + X[n] * beta2; 
       y[n] ~ lognormal(mu, sigma_y);
   }
  }

```

Message:  
`Initialization between (-2, 2) failed after 100 attempts. Try specifying initial values, reducing ranges of constrained values, or reparameterizing the model.`

Any idea what is wrong?

#### Data

I have checked and there is no missing values in the input matrix and y.

Example:  
Raw:

> ```
> word y 
> A 0.1
> A 0.2
> B 0.3
> C 0.5
> 
> ```

Transform word into dummy variables

Input matrix (692 x 655)

> ```
> word1 word2 ... word655
> row1 1 0 0
> row2 1 0 0
> .
> .
> row692 0 1 0
> 
> ```

y = 692  
J = groups (number of distinct words) = 655 (see input matrix)

Addition question-  
In both models, how can I get errors for each word/random effect?  
Right now I get x iteration values of sigma\_a as well as sigma\_y.  
Is it possible to get 655 sigma\_a for each word?

---

<div class="post-metadata">

**Author:** ![bbbales2](https://yyz2.discourse-cdn.com/flex030/user_avatar/discourse.mc-stan.org/bbbales2/32/77_2.png) [@bbbales2](https://discourse.mc-stan.org/u/bbbales2)\
**Post date:** [October 29, 2017, 4:12pm UTC](https://discourse.mc-stan.org/t/base-type-mismatch-what-is-wrong/2343/6 "2017-10-29T16:12:10Z")

</div>

Hmm, you have the right constraints on sigma\_a and sigma\_y.

I’d guess, if anything, you should check and make sure all the y elements are greater than zero. That’s one of the requirements for a lognormal.

> In both models, how can I get errors for each word/random effect? Right now I get x iteration values of sigma\_a as well as sigma\_y. Is it possible to get 655 sigma\_a for each word?

Ooof, honestly, with 692 data points and 655 coefficients, you’re going to have a rough time with about anything. That’s not very much data.

What kind of model is this?

---

<div class="post-metadata">

**Author:** ![pfan2017](https://avatars.discourse-cdn.com/v4/letter/p/e8c25b/32.png) [@pfan2017](https://discourse.mc-stan.org/u/pfan2017)\
**Post date:** [October 31, 2017, 3:31pm UTC](https://discourse.mc-stan.org/t/base-type-mismatch-what-is-wrong/2343/7 "2017-10-31T15:31:52Z")

</div>

You’re right- I had some negative values in the y’s. Now that’s fixed.

I forgot to mention that 692 data points is a subset of my data. I tried a fixed effects model on the data using a few thousand data points, and it took about an hour. I wanted to shorten the run time so I’ve been using a smaller sample size.

My question was in the data model, the output of sigma\_a is one number for each iteration. How can I make it into length J (number of words) vector?

---

<div class="post-metadata">

**Author:** ![bbbales2](https://yyz2.discourse-cdn.com/flex030/user_avatar/discourse.mc-stan.org/bbbales2/32/77_2.png) [@bbbales2](https://discourse.mc-stan.org/u/bbbales2)\
**Post date:** [October 31, 2017, 8:32pm UTC](https://discourse.mc-stan.org/t/base-type-mismatch-what-is-wrong/2343/8 "2017-10-31T20:32:44Z")

</div>

> one number for each iteration

You mean like J `sigma_a`s instead of just one?

```stan
parameters {
  ...
  vector<lower=0.0>sigma_a[J] sigma_a;
}

```

Should do the trick as far as getting it programmed, but it doesn’t make sense to me.

So the mean of y comes from:

1. beta1, a base level
2. u, which is a different offset per group
3. X \* b, effect of other covariates

Your prior on u, normal(0, sigma\_a) presumably means that you expect the `u` parameters to be at least in the same range as each other. Seems like a reasonable sort of regularizing assumption (you’d need to put a prior on sigma\_a or fix it to actually make this happen though).

Given this, I’m not sure what giving each `u` its own standard deviation would get you.

Probly the bigger issue with this model is the lack of priors on sigma\_a, sigma\_y, beta1, and beta2. Are your estimates working out without them?