betareg               package:betareg               R Documentation

_F_i_t_t_i_n_g _b_e_t_a _r_e_g_r_e_s_s_i_o_n _m_o_d_e_l_s

_D_e_s_c_r_i_p_t_i_o_n:

     'betareg' is used to fit a regression model where the response is
     beta distributed using a parameterization of the beta law that is
     indexed by mean and dispersion parameters.

_U_s_a_g_e:

     betareg(formula, link = "logit", data)

_A_r_g_u_m_e_n_t_s:

 formula: a symbolic description of the model to be fit: response ~
          covariates. 

    link: a link function; the following links are available: logit,
          probit and cloglog. The default link function is logit. 

    data: the 'data argument': a data frame containing the data. 

_D_e_t_a_i_l_s:

     The model is useful for situations where the variable of interest
     is continuous, restricted to the standard unit interval (0,1), and
     related to other variables through a regression structure. The
     regression parameters of the beta regression model are
     interpretable in terms of the mean of the response and, when the
     logit link is used, of an odds ratio, unlike the parameters of a
     linear regression that employs a transformed response. Estimation
     is performed by maximum likelihood. The log-likelihood function is
     maximized using the quasi-Newton BFGS algorithm with analytical
     first derivatives; the choice of initial values follows the
     proposal made by Ferrari and Cribari-Neto (2004).

_V_a_l_u_e:

     The function returns an object of class 'betareg' or 'lm'. The
     function 'summary' is used to obtain an estimate of the precision
     parameter (phi), and the pseudo R2 value.

_A_u_t_h_o_r(_s):

     Alexandre de Bustamante Simas, Instituto de Matemtica Pura e
     Aplicada (alesimas@impa.br).

_R_e_f_e_r_e_n_c_e_s:

     FERRARI, S.L.P., CRIBARI-NETO, F. (2004). Beta regression for
     modeling rates and proportions. Journal of Applied Statistics, v.
     31, n. 7, p. 799-815.

_S_e_e _A_l_s_o:

     'br.fit', 'summary.betareg', 'predict.betareg',
     'residuals.betareg'

_E_x_a_m_p_l_e_s:

     data(pratergrouped)
     fit1 <- betareg(oil ~ batch1 + batch2 + batch3 + batch4 + batch5 + 
     batch6 + batch7 + batch8 + batch9 + temp, data=pratergrouped)
     fit2 <- betareg(oil ~ batch1 + batch2 + batch3 + batch4 + batch5 + batch6 + 
     batch7 + batch8 + batch9 + temp, link = "probit", data=pratergrouped)
     summary(fit1)
     par(mfrow=c(2,2))
     plot(fit1)

