fit <- stan_polr (FWS1 ~ Ethnic1 + Fam1 + Eco1+ Health1 + Safety1

+ Community1 + Religios1 + Housing1, data = as.data.frame (BayesOrdinal1),

method = “logistic”, prior = R2(0.2, “mean”),

prior_counts = dirichlet(1), init_r = 0.1, seed = 12345,

algorithm = “sampling”)

Error: ‘1’ is not a supported link for family ‘dirichlet’.

Supported links are: ‘logit’

Dear rstanarm and brms users

How pass this error? what is the reason?

How to specify the prior? How to calculate AIC?

Please need your help.

Thank you

Hi Yousif!

Hm, I don’t see where this is coming from. Could you post a subset of your data set and the exact script that produced this error?

Did you already read through the vignette for rstanarm::stan_polr?

Btw, AIC is not a Baysian qunatity. It is based on the Maximum Likelihood Estimator, which Stan doesn’t compute (at least not with `sampling`

). See, for example, this thread and numerous other threats in this forum for alternatives (hint: `loo`

package).

Cheers!

Max

Daer Max

Please find the attached data set and try to check the reason of the error.

How can we used the past data as a prior to estimate the parameters of the Bayesian ordinal regression of these data?

Hi Yousif!

I used the `clipr::read_clip_tbl()`

function to get in the data, but next time it’d probably be easier if you could just upload the `csv`

file using the *Upload* button in the menu. ;)

Anyway… the model actually ran fine for me:

```
df <- clipr::read_clip_tbl()
library(tidyverse)
df <- df %>% mutate(FWS1 = ordered(FWS1, levels = c("Low", "Moderate", "High")))
library(rstanarm)
options(mc.cores = parallel::detectCores())
fit <- stan_polr(
FWS1 ~ Ethnic1 + Fam1 + Eco1 + Health1 + Safety1 + Community1 + Religios1 + Housing1,
data = df,
method = "logistic",
prior = R2(0.2, "mean"),
prior_counts = dirichlet(1),
init_r = 0.1,
seed = 12345,
algorithm = "sampling"
)
summary(fit)
```

## Fit summary:

```
Model Info:
function: stan_polr
family: ordered [logistic]
formula: FWS1 ~ Ethnic1 + Fam1 + Eco1 + Health1 + Safety1 + Community1 +
Religios1 + Housing1
algorithm: sampling
sample: 4000 (posterior sample size)
priors: see help('prior_summary')
observations: 49
Estimates:
mean sd 10% 50% 90%
Ethnic1Chinese 0.1 0.5 -0.5 0.1 0.7
Ethnic1Indian 0.1 0.6 -0.7 0.1 1.0
Fam1Low -0.1 1.2 -1.6 -0.1 1.5
Fam1Moderate 0.0 0.7 -0.9 0.0 0.8
Eco1Low 0.0 0.5 -0.6 0.0 0.6
Eco1Moderate -0.1 0.3 -0.6 -0.1 0.3
Health1Low -0.2 0.5 -0.8 -0.2 0.5
Health1Moderate 0.1 0.5 -0.6 0.1 0.7
Safety1Low -0.5 1.4 -2.4 -0.5 1.3
Safety1Moderate -0.1 0.6 -0.8 -0.1 0.6
Community1Low 0.2 0.7 -0.7 0.2 1.2
Community1Moderate -0.1 0.7 -0.9 -0.1 0.8
Religios1Low -0.2 0.5 -0.9 -0.2 0.4
Religios1Moderate 0.0 0.6 -0.8 0.0 0.8
Housing1Low 0.0 0.5 -0.7 0.0 0.6
Housing1Moderate -0.1 0.4 -0.6 -0.1 0.4
Low|Moderate -2.6 0.5 -3.3 -2.6 -1.9
Moderate|High -1.8 0.4 -2.4 -1.8 -1.3
Fit Diagnostics:
mean sd 10% 50% 90%
mean_PPD:Low 0.1 0.1 0.0 0.1 0.2
mean_PPD:Moderate 0.1 0.1 0.0 0.1 0.1
mean_PPD:High 0.8 0.1 0.7 0.8 0.9
The mean_ppd is the sample average posterior predictive distribution of the outcome variable (for details see help('summary.stanreg')).
MCMC diagnostics
mcse Rhat n_eff
Ethnic1Chinese 0.0 1.0 5056
Ethnic1Indian 0.0 1.0 5190
Fam1Low 0.0 1.0 4929
Fam1Moderate 0.0 1.0 4690
Eco1Low 0.0 1.0 4629
Eco1Moderate 0.0 1.0 5441
Health1Low 0.0 1.0 4424
Health1Moderate 0.0 1.0 5796
Safety1Low 0.0 1.0 4895
Safety1Moderate 0.0 1.0 4707
Community1Low 0.0 1.0 4160
Community1Moderate 0.0 1.0 4910
Religios1Low 0.0 1.0 5215
Religios1Moderate 0.0 1.0 5383
Housing1Low 0.0 1.0 5299
Housing1Moderate 0.0 1.0 4977
Low|Moderate 0.0 1.0 5183
Moderate|High 0.0 1.0 5239
mean_PPD:Low 0.0 1.0 4623
mean_PPD:Moderate 0.0 1.0 4124
mean_PPD:High 0.0 1.0 4300
log-posterior 0.1 1.0 1474
For each parameter, mcse is Monte Carlo standard error, n_eff is a crude measure of effective sample size, and Rhat is the potential scale reduction factor on split chains (at convergence Rhat=1).
```

I don’t know where your error comes from… hm…

I am so sorry I though you want to see only how the data looks like.

[quote=“Max_Mantei, post:5, topic:13511”]

Anyway… the model actually ran fine for me:

Thank you for your interest.

I am also got the same result when I did the first run. After that I got an error as I mentioned. However, when I removed " prior_count=dirichlet(1) " it works.

What is the difference if we use " prior_count=dirichlet(1) " or remove it from stan_polr() ?

How to use past data as a prior ? which prior we can use instead of R2(0.2,“mean”) for this data?

Highly appreciated

No worries! I should have been more clear. :)

Did you include the prior specification in your first run?

This is all very well documented in the `rstanarm`

vignettes. If you remove `prior_count=dirichlet(1)`

, it would not really change anything, since the default prior *is* `dirichlet(1)`

. Also, you can can obviously change the R^2 prior location: The more explanatory power you think your predictors have (jointly), the higher should be your prior R^2. You can also specify `prior=NULL`

which will result in flat priors (uniform on the real number line) for the regression coefficients. This is generally not advisable.

You can verify that the default prior for the counts is indeed \text{Dirichlet(1)} and that `prior=NULL`

yields flat priors:

```
fit_default <- stan_polr(
FWS1 ~ Ethnic1 + Fam1 + Eco1 + Health1 + Safety1 + Community1 + Religios1 + Housing1,
data = df,
method = "logistic",
prior = NULL,
init_r = 0.1,
seed = 12345,
algorithm = "sampling"
)
```

Now call `prior_summary`

on the `fit_default`

object:

```
> prior_summary(fit_default)
Priors for model 'fit_default'
------
Coefficients
~ flat
Counts
~ dirichlet(concentration = [1,1,1])
------
See help('prior_summary.stanreg') for more details
```

You should really check out the `rstanarm`

vignettes. They are awesome!

Dear Max

Thank you so much

I have read rstanarm` vignettes and I have check it.

Now my question is how to used the past data (data of 2011) as a prior to estimate the parameters of data of 2016.

How to use covariance matrix as a prior?

Thank you

I can only recommend what @bbbales2 said in the other thread. I hope I don’t sound harsh here, but I think the problems are a bit more conceptual. I know that Bayesian reasoning is often sold as updating prior beliefs after seeing data, then looking at new data with the new prior and update again and so on. But this is not what is typically done Bayesian Statistics. At least I don’t think it is helpful to think about it that way. I guess that is what @bbbales2 was trying to convey (just estimate your data jointly!).

I’d recommend you pick up a good textbook and familiarize yourself a bit more with this approach to Bayesian modeling. I always recommend the Gelman and Hill textbook or Statistical Rethinking by Richard McElreath.

1 Like

Thank you

I will try to use this.

What about using prior of covariance matrix that mentioned in rstanarm?

How to use it as a prior?

greatly appreciated