Four Parameters: Categorical

Author

Tulika Punia

Warning: package 'brms' was built under R version 4.4.1
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Using data from the National Election Studies survey of US citizens, we aim to examine the relationship between voter preference and sex in the 1992 Presidential election. We have combined information from the Preceptor Table and the data to create a comprehensive Population Table. However, the assumption of stability might be problematic if voter preferences or demographics shifted between the data collection and the Preceptor Table. One Qol was the vote result, as it could have been used to determine how gender impacted who was voted for. \[\begin{aligned} \rho_{clinton} &=& \frac{e^{\beta_{0, clinton} + \beta_{1, clinton} male}}{1 + e^{\beta_{0, clinton} + \beta_{1, clinton} male}}\\ \rho_{perot} &=& \frac{e^{\beta_{0, perot} + \beta_{1, perot} male}}{1 + e^{\beta_{0, perot} + \beta_{1, perot} male}}\\ \rho_{bush} &=& 1 - \rho_{clinton} - \rho_{perot} \end{aligned}\]
Warning in tidy.brmsfit(x, ..., effects = "fixed"): some parameter names
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It could be the case if that type of model does not implement these methods.
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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