mmlcrcomponentinit           package:mmlcr           R Documentation

_I_n_i_t_i_a_l_i_z_e _a_n '_m_m_l_c_r' _c_o_m_p_o_n_e_n_t

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

     Used internally by 'mmlcr' to initialize an 'mmlcr' component. The
     function is generic.

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

     mmlcrcomponentinit(object, n.groups,  prob, data, grouping)

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

  object: see details below 

n.groups: the number of classes. Provided by 'mmlcr'. 

    prob: a data.frame of (initial) posterior probabilities of class
          membership. Provided by 'mmlcr'. 

    data: a data.frame. Provided by 'mmlcr'. 

grouping: the variable of data indicating subject id. Provided by
          'mmlcr'. 

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

     The object argument is a list, originally provided to the 'mmlcr'
     function as a  component of a list in the components argument. At
     a minimum, that list consists  of a formula of the form 'resp ~
     covars', where resp is the response variable for  that component
     and covars are any additional covariates ('resp ~ 1' is commonly 
     used), and a class. For longitudinal responses, the covariates
     should include  the chronological variable, e.g., 'resp ~
     poly(age, 2)'. Implemented classes include  'cnormlong',
     'cnormonce', 'multinomlong', 'multinomonce', 'nblong', 'nbonce', 
     'normlong', 'normonce', 'poislong', and 'poisonce'. The *'long'
     classes are for longitudinal responses, while the *'once' classes
     are for one-time responses. So that a rectangular data structure
     can be used for the data argument in 'mmlcr', the *'once' classes
     use just the first occurence of a response for each subject.

     The 'cnorm'* classes are for censored normal distributions. They
     take a 'min' and 'max' argument for the minimum and maximum
     possible observed value. The 'multinom'* classes are for
     multinomial, or categorical responses. The response variable
     should be a factor.  The 'nb'* classes are for  negative binomial
     models (the usual overdispersed Poisson model). The 'norm'*
     classes  are for normal models, and the 'pois'* classes are for
     Poisson models. For the 'cnorm'*  and 'norm'* classes the minimum
     and maximum variances are constrained to be within a  factor of
     100 of each other. For the 'pois'* classes an overdispersion
     estimate is  calculated but not used.

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

     an 'mmlcr' component object.

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

     'mmlcr', 'mmlcrObject'

