lmList                 package:lme4                 R Documentation

_L_i_s_t _o_f _l_m _O_b_j_e_c_t_s _w_i_t_h _a _C_o_m_m_o_n _M_o_d_e_l

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

     The 'data' argument is split according to the levels of the
     grouping factor 'g' and individual 'lm' or 'glm' fits are obtained
     for each 'data' partition, using the model defined in 'object'.

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

     lmList(formula, data, family, subset, weights,
            na.action, offset, pool, ...)

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

 formula: a linear formula object of the form 'y ~ x1+...+xn | g'. In
          the formula object, 'y' represents the response, 'x1,...,xn'
          the covariates, and 'g' the grouping factor specifying the
          partitioning of the data according to which different 'lm'
          fits should be performed.

    data: a data frame in which to interpret the variables named in
          'object'.

  family: an optional family specification for a generalized linear
          model.

 weights: an optional vector of weights to be used in the fitting
          process.

  subset: an optional vector specifying a subset of observations to be
          used in the fitting process.

na.action: a function which indicates what should happen when the data
          contain 'NA's.  The default is set by the 'na.action' setting
          of 'options', and is 'na.fail' if that is unset.  The
          "factory-fresh" default is 'na.omit'.

  offset: this can be used to specify an _a priori_ known component to
          be included in the linear predictor during fitting.

    pool: an optional logical value that is preserved as an attribute
          of the returned value.  This will be used as the default for
          'pool' in calculations of standard deviations or standard
          errors for summaries.

     ...: optional arguments to be passed to the model-fitting
          function.

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

     an object of class '"lmList"', which is a list of 'lm' objects
     with as many components as the number of groups defined by the
     grouping factor.

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

     'lm'

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

     (fm1 <- lmList(Reaction ~ Days | Subject, sleepstudy))

