#title Beta regression

종속변수 y가 분자/분모 형태일 때 써먹는다.

{{{
data("FoodExpenditure", package = "betareg")
fe_beta <- betareg(I(food/income) ~ income + persons, data = FoodExpenditure)
summary(fe_beta)
}}}

결과
{{{
> summary(fe_beta)

Call:
betareg(formula = I(food/income) ~ income + persons, data = FoodExpenditure)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-2.7818 -0.4445  0.2024  0.6852  1.8755 

Coefficients (mean model with logit link):
             Estimate Std. Error z value Pr(>|z|)    
(Intercept) -0.622548   0.223854  -2.781 0.005418 ** 
income      -0.012299   0.003036  -4.052 5.09e-05 ***
persons      0.118462   0.035341   3.352 0.000802 ***

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)    35.61       8.08   4.407 1.05e-05 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 45.33 on 4 Df
Pseudo R-squared: 0.3878
Number of iterations: 28 (BFGS) + 4 (Fisher scoring)
}}}


{{{
data("GasolineYield", package = "betareg")
gy <- betareg(yield ~ batch + temp, data = GasolineYield)
summary(gy)
}}}