model.frame              package:stats              R Documentation

_E_x_t_r_a_c_t_i_n_g _t_h_e "_E_n_v_i_r_o_n_m_e_n_t" _o_f _a _M_o_d_e_l _F_o_r_m_u_l_a

_D_e_s_c_r_i_p_t_i_o_n:

     'model.frame' (a generic function) and its methods return a
     'data.frame' with the variables needed to use 'formula' and any
     '...' arguments.

_U_s_a_g_e:

     model.frame(formula, ...)

     ## Default S3 method:
     model.frame(formula, data = NULL,
                subset = NULL, na.action = na.fail,
                drop.unused.levels = FALSE, xlev = NULL, ...)

     ## S3 method for class 'aovlist':
     model.frame(formula, data = NULL, ...)

     ## S3 method for class 'glm':
     model.frame(formula, ...)

     ## S3 method for class 'lm':
     model.frame(formula, ...)

_A_r_g_u_m_e_n_t_s:

 formula: a model 'formula' or 'terms' object or an R object.

    data: 'data.frame', list, 'environment' or object coercible to
          'data.frame' containing the variables in 'formula'.  Neither
          a matrix nor an array will be accepted.

  subset: a specification of the rows to be used: defaults to all rows.
          This can be any valid indexing vector (see '[.data.frame')
          for the rows of 'data' or if that is not supplied, a data
          frame made up of the variables used in 'formula'.

na.action: how 'NA's are treated. The default is first, any 'na.action'
          attribute of 'data', second a 'na.action' setting of
          'options', and third 'na.fail' if that is unset.  The
          "factory-fresh" default is 'na.omit'.

drop.unused.levels: should factors have unused levels dropped? Defaults
          to 'FALSE'.

    xlev: a named list of character vectors giving the full set of
          levels to be assumed for each factor.

     ...: further arguments such as 'data', 'na.action', 'subset'.  Any
          additional arguments such as 'offset' and 'weights' which
          reach the default method are used to create further columns
          in the model frame, with parenthesised names such as
          '"(offset)"'.

_D_e_t_a_i_l_s:

     Exactly what happens depends on the class and attributes of the
     object 'formula'.  If this is an object of fitted-model class such
     as '"lm"', the method will either returned the saved model frame
     used when fitting the model (if any, often selected by argument
     'model = TRUE') or pass the call used when fitting on to the
     default method.  The default method itself can cope with rather
     standard model objects such as those of classes '"lqs"' and
     '"ppr"' if no other arguments are supplied.

     The rest of this section applies only to the default method.

     If either 'formula' or 'data' is already a model frame (a data
     frame with a '"terms"' attribute and the other is missing, the
     model frame is returned.  Unless 'formula' is a terms object,
     'terms' is called on it.  (If you wish to use the 'keep.order'
     argument of 'terms.formula', pass a terms object rather than a
     formula.)

     All the variables in 'formula', 'subset' and in '...' are looked
     for first in 'data' and then in the environment of 'formula' (see
     the help for 'formula()' for further details) and collected into a
     data frame. Then the 'subset' expression is evaluated, and it is
     is used as a row index to the data frame. Then the 'na.action'
     function is applied to the data frame (and may well add
     attributes).  The levels of any factors in the data frame are
     adjusted according to the 'drop.unused.levels' and 'xlev'
     arguments.

_V_a_l_u_e:

     A 'data.frame' containing the variables used in 'formula' plus
     those specified '...'.

_R_e_f_e_r_e_n_c_e_s:

     Chambers, J. M. (1992) _Data for models._ Chapter 3 of
     _Statistical Models in S_ eds J. M. Chambers and T. J. Hastie,
     Wadsworth & Brooks/Cole.

_S_e_e _A_l_s_o:

     'model.matrix' for the "design matrix", 'formula' for formulas 
     and 'expand.model.frame' for model.frame manipulation.

_E_x_a_m_p_l_e_s:

     data(cars)
     data.class(model.frame(dist ~ speed, data = cars))

