# Names of regression parameters in brm_multiple

I just ran my first ordinal regression model with brm_multiple. The predictor variable is year, an ordinal variable whose possible values are 2007,2009,2011,2013,2015, 2017 and 2019. 2007 is the reference year, so I expected to find the parameter names to include 6 intercepts, labeled [1] through [6], then 6 change parameters named 2009,2011,2013, 2015, 2017, 2019.

Instead, the change parameters are named:
year.L
year.Q
year.C
yearE4
yearE5
yearE6

Can anyone explain this?
I’m assuming these are the parameters I expected, in the proper order, just with different names. But I have no idea where the names came from or what they refer to. There’s no L,Q,C or E in my data.

original call was:
screens_model_1a<-brm_multiple(formula=tvt_asd~1+year,data=yrbs.complex.mdimp,family=cumulative(“probit”),prior=prior_screen,combine=TRUE,chains=1,file=“screens_model1a”)

The datasets, imputed in mice, are in yrbs_complex_mdimp.
tvt_asd, the outcome variable, is a 7-level ordinal variable (multiple choice response on a survey for number of hours spent watching TV on an average school day)
The prior referred to is:
prior_screen<-prior(normal(0,5), class=“b”) + prior (normal(0,5), class=“Intercept”)

Thanks!

Hi!

These are contrasts based on orthogonal polynomials, as it is standard for ordinal predictor (see `contr.poly`).

You might:

1. change the contrasts of the variable to forward or backward difference coding,

2. use the `emmeans` package to get estimated marginal means for the change points or

3. enclose `year` in `mo(…)` to treat is as a monotonic effect

Cheers,
Pascal

Amazing! I’ve just been working from the tutorial, where it looked like the names would be straightforward. I bet monotonic effects will do the trick. Thank you so much.