We created a preceptor table with the data that we are looking for. We then pulled data from nes, and used the 1992 voting responses for a our data of a population table. We are looking to answer the question, whether sex affects voter behavior for the 1992 presidential election. We took the data, and questions whether the data is representative, random, and not biased. We created a Data generating mechanism to predict the values and see the relationship between gender and voter behavior. We found out the Male were less likely to vote for Clinton and more likely to vote for Perot, and women were more likely to vote for Clinton. After creating the DGM, we used the posterior results to create graphs to depict how voter behavior was influence by gender in each candidate.
\[\begin{aligned}
\rho_{clinton} &=& \frac{e^{\beta_{0, clinton} + \beta_{1, clinton} male}}{1 + e^{\beta_{0, clinton} + \beta_{1, clinton} male}}\\
\rho_{perot} &=& \frac{e^{\beta_{0, perot} + \beta_{1, perot} male}}{1 + e^{\beta_{0, perot} + \beta_{1, perot} male}}\\
\rho_{bush} &=& 1 - \rho_{clinton} - \rho_{perot}
\end{aligned}\]
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.).
| 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 |
Adding missing grouping variables: `.row`
