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. 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).

_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. 

_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, Department of Statistics, Federal
     University of Pernambuco (alexandredbs@cox.de.ufpe.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,
     forthcoming.

_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)
     attach(pratergrouped)
     y <- V11/100
     X <- cbind(V1,V2,V3,V4,V5,V6,V7,V8,V9,V10)
     fit1 <- betareg(y ~ X)
     fit2 <- betareg(y ~ X, link = "probit")
     summary(fit1)
     par(mfrow=c(2,2))
     plot(fit1)

