zeroinfl                package:pscl                R Documentation

_Z_e_r_o-_i_n_f_l_a_t_e_d _r_e_g_r_e_s_s_i_o_n _m_o_d_e_l_s _f_o_r _c_o_u_n_t _d_a_t_a

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

     Fit zero-inflated regression models for count data via maximum
     likelihood

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

     zeroinfl(count = y ~ ., x = ~1, z = ~1,
             data = list(),
             link = "logit", dist = "poisson",
             method = "BFGS", trace = FALSE, maxit = 50000,
             na.action = na.omit)

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

   count: count being modeled, passed as the left-hand side of a
          'formula'; lowest count must be zero for zero-inflated models

       x: right-hand side of a 'formula' giving covariates for the
          count part of the model

       z: right-hand side of a 'formula' giving covariates for the
          zero-inflated part of the model

    data: a data frame

    link: link function for zero-inflated part of the model (choices
          are 'logit' (default) or 'probit')

    dist: type of count model, '"poisson"' (default) or '"negbin"'

  method: method for maximizing the log-likelihood function, only
          '"BFGS"' and '"Nelder-Mead"' are supported

   trace: logical, if 'TRUE', display progress of maximization

   maxit: maximum number of iterations in maximization

na.action: method for handling missing data, default is 'na.omit'

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

     Zero-inflated count models are a type of two-component mixture
     model, with a component for zero counts, and the other component
     for the positive counts.  Poisson or negative-binomial models are
     used for the count component of the model; logit or probit is
     typically used to model the probability of a zero-count.  'optim'
     is used to find maximum likelihood estimates and to compute a
     Hessian matrix after convergence.

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

     an object of class 'zeroinfl', i.e., a list with components
     including 

   stval: start values used in optimzation

     par: Maximum likelihood estimates

 hessian: Matrix of second derivatives of the log-likelihood function
          evaluated at the MLEs; computed numerically by 'optim'

     llh: value of the log-likelihood function at the MLEs

       y: vector of counts actually fitted (after any screeing of
          missing data)

       x: matrix of covariates used in fitting the count model

       z: matrix of covariates used in fitting the zero-inflated
          component

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

     Simon Jackman <jackman@stanford.edu>

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

     Lambert, Diane.  1992. "Zero-Inflated Poisson Regression, With an
     Application to Defects in Manufacturing." 
     _Technometrics_.V34(1):1-14

     Cameron, A. Colin and Pravin K. Trevedi. 1998. _Regression
     analysis of count  data._ New York: Cambridge University Press.

     Long, J. Scott. 1997. _Regression Models for Categorical and
     Limited Dependent  Variables._ Number 7 in Advanced Quantitative
     Techniques in the Social  Sciences. Thousand Oaks, California:
     Sage.

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

     'summary.zeroinfl', 'predict.zeroinfl', 'hurdle', 'glm.nb'

     Methods are supplied for the generic functions 'coef' and
     'logLik', for objects of class '"zeroinfl"'.

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

     data(bioChemists)
     zip <- zeroinfl(count=art ~ .,
                     x = ~ fem + mar + kid5 + phd + ment,
                     z = ~ fem + mar + kid5 + phd + ment,
                     data=bioChemists,trace=TRUE)
     summary(zip)

     zinb <- zeroinfl(count=art ~ .,
                      x = ~ fem + mar + kid5 + phd + ment,
                      z = ~ fem + mar + kid5 + phd + ment,
                      dist="negbin",
                      data=bioChemists,trace=TRUE)
     summary(zinb)

