Adding missing grouping variables: `.row`
Four Parameters: Categorical
Using a data from the National Election Studies survey, we seek to understand the relationship between sex and voting in the 1992 US Presidential election. Our results may be biased, as votes may not be reflective of what voters indicated in the survey. We created pres_vote, a multinomial logistic regression model. Women are more likely to support Clinton than men. About 48% of women claim to support Clinton, though that number could be between 43% and 58%.
[sentence with the confidence interval here]
\[\begin{aligned} \rho_{clinton} &=& \frac{e^{\beta_{0, clinton} + \beta_{1, clinton} male}}{1 + e^{\beta_{0, clinton} + \beta_{1, clinton} male}}\\ \rho_{perot} &=& \frac{e^{\beta_{0, perot} + \beta_{1, perot} male}}{1 + e^{\beta_{0, perot} + \beta_{1, perot} male}}\\ \rho_{bush} &=& 1 - \rho_{clinton} - \rho_{perot} \end{aligned}\]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 | 0.30, 0.60 |
| muPerot_(Intercept) | -0.86 | -1.1, -0.65 |
| muClinton_sexMale | -0.25 | -0.47, -0.04 |
| muPerot_sexMale | 0.42 | 0.14, 0.71 |
| 1
CI = Credible Interval |
||