

lmList(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 func-
          tion `lmList.groupedData' is documented sepa-
          rately.

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

   level: an optional integer specifying the level of group-
          ing 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 mes-
          sage 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 sum-
          maries.

_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_:

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

