Four Parameters: Categorical

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

Warning in tidy.brmsfit(x, ..., effects = "fixed"): some parameter names
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✖ 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.
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Characteristic	Beta	95% CI 1
muClinton_(Intercept)	0.45	0.31, 0.60
muPerot_(Intercept)	-0.85	-1.1, -0.64
muClinton_sexMale	-0.25	-0.48, -0.03
muPerot_sexMale	0.42	0.14, 0.69
1 CI = Credible Interval

Adding missing grouping variables: `.row`

Using the data from the National Election Studies survey of US citizens, we will be exploring whether there is a relationship between sex and voting behavior. Our analysis might encounter variability due to discrepancies in self-reported voting behavior, where respondents may claim to have voted for one candidate but actually voted for another. Preliminary results indicate that males are more likely to vote for a specific candidate. A key quantity of interest (QoI) in our study is the proportion of males who voted for this candidate, estimated at 60% with a 95% confidence interval ranging from 55% to 65%. This measure of uncertainty reflects the potential variability in our estimate due to sampling and reporting errors.