Adding missing grouping variables: `.row`
Four Parameters: Categorical
We are analyzing a dataset from the American National Election Studies to determine if sex was a factor in voting for the 1992 presidential election. One problem is that the sample is not random enough to accurately represent the population. We made a multinomial logistic regression model to develop approximate intercepts for the votes each candidate get. In our model, women are more likely to support Clinton. Women are around 53% likely to support Clinton, but it could be around 48% or 58%
\[\begin{aligned} \rho_{clinton} &=& \frac{e^{\beta_{0, clinton} + \beta_{1, clinton} male}}{1 + e^{\beta_{0, clinton} + \beta_{1, clinton} male}}\\ \rho_{perot} &=& \frac{e^{\beta_{0, perot} + \beta_{1, perot} male}}{1 + e^{\beta_{0, perot} + \beta_{1, perot} male}}\\ \rho_{bush} &=& 1 - \rho_{clinton} - \rho_{perot} \end{aligned}\]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) | -0.85 | -1.1, -0.64 |
| muClinton_sexMale | -0.25 | -0.48, -0.03 |
| muPerot_sexMale | 0.42 | 0.14, 0.69 |
| 1
CI = Credible Interval |
||