Four Parameters: Categorical

Adding missing grouping variables: `.row`

With the data of the American National Election Studies, (ANES) we are able to examine the data of a voter’s preference and sex, and formed a Preceptor Table as a result. A problem that casts doubt on my approach is the difference of how people respond or not in the surveys. The model is a multinomial logistic regression model. The functional form is a linear regression model, and the dependent variable is expected vote probability. The independent variable is sex. It is seen that women are supportive toward Clinton. About 53% of women claim to support Clinton, although that number could be as high as 58% or as low as 48%.

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

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)	0.46	0.31, 0.61
muPerot_(Intercept)	-0.85	-1.1, -0.64
muClinton_sexMale	-0.26	-0.48, -0.04
muPerot_sexMale	0.42	0.13, 0.71
1 CI = Credible Interval