lmList                 package:nlme                 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:

     `Data' is partitioned according to the levels of the grouping
     factor `g' and individual `lm' fits are obtained for each `data'
     partition, using the model defined in `object'.

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

     lmList(object, data, level, na.action, pool)

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

  object: either a linear formula object of the form `y ~ x1+...+xn |
          g' or a `groupedData' object. 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.
          The grouping factor `g' may be omitted from the formula, in
          which case the grouping structure will be obtained from
          `data', which must inherit from class `groupedData'. The
          method function  `lmList.groupedData' is documented
          separately. 

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

   level: an optional integer specifying the level of grouping to be
          used when  multiple nested levels of grouping are present. 

na.action: a function that indicates what should happen when the data
          contain `NA's.  The default action (`na.fail') causes
          `lmList' to print an error message and terminate if there are
          any incomplete observations. 

    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. 

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

     a list of `lm' objects with as many components as the number of
     groups defined by the grouping factor. Generic functions such as
     `coef', `fixed.effects', `lme', `pairs', `plot', `predict',
     `random.effects', `summary', and `update' have methods that can be
     applied to an `lmList' object.

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

     `lm', `lme.lmList'.

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

     data(Orthodont)
     fm1 <- lmList(distance ~ age | Subject, Orthodont)
     summary(fm1)

