Four Parameters: Categorical

Adding missing grouping variables: `.row`

Using data from the National Election Studies (NES) survey of US citizens, we seek to understand the relationship between voter preference and sex in the 1992 Presidential election. Our results might be biased by differential non-response among different categories of voters that go unobserved. We modeled pres_vote, a character variable, as a multinomial logistic regression model. The general direction of our independent and dependent variable is that females are more likely to vote for Democrats. The QoI is 0.45, meaning around 48% to 58% of female voters will vote for Clinton, however, this cannot be completely accurate, and requires an interval of confidence.

\[\begin{aligned} \rho_{clinton} &=& \frac{e^{\beta_{0, clinton} + \beta_{1, clinton} male}}{1 + e^{\beta_{0, clinton} + \beta_{1, clinton} male}}\\ \rho_{perot} &=& \frac{e^{\beta_{0, perot} + \beta_{1, perot} male}}{1 + e^{\beta_{0, perot} + \beta_{1, perot} male}}\\ \rho_{bush} &=& 1 - \rho_{clinton} - \rho_{perot} \end{aligned}\]

Warning in tidy.brmsfit(x, ..., effects = "fixed"): some parameter names
contain underscores: term naming may be unreliable!

✖ Unable to identify the list of variables.

This is usually due to an error calling `stats::model.frame(x)`or `stats::model.matrix(x)`.
It could be the case if that type of model does not implement these methods.
Rarely, this error may occur if the model object was created within
a functional programming framework (e.g. using `lappy()`, `purrr::map()`, etc.).

Characteristic	Beta	95% CI 1
muClinton_(Intercept)	0.45	0.31, 0.60
muPerot_(Intercept)	-0.86	-1.1, -0.64
muClinton_sexMale	-0.25	-0.48, -0.04
muPerot_sexMale	0.42	0.14, 0.71
1 CI = Credible Interval