Adding missing grouping variables: `.row`
Four Parameters:Categorical
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. We modeled pres_vote, a character variable, as a multinomial logistic regression model. Women are most likely to support 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) | 0.45 | 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 |
||