nlsList                 package:nlme                 R Documentation

_L_i_s_t _o_f _n_l_s _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 defined in `model' and individual `nls' fits are obtained
     for each `data' partition, using the model defined in `model'.

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

     nlsList(model, data, start, control, level, na.action, pool)

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

   model: either a nonlinear model formula, with the response on the
          left of a `~' operator and an expression involving
          parameters, covariates, and a grouping factor separated by
          the `|' operator on the right, or a `selfStart' function. 
          The method function `nlsList.selfStart' is documented
          separately. 

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

   start: an optional named list with initial values for the parameters
          to be estimated in `model'. It is passed as the `start'
          argument to each `nls' call and is required when the
          nonlinear function in `model' does not inherit from class
          `selfStart'. 

 control: a list of control values passed as the `control' argument to
          `nls'. Defaults to an empty list. 

   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
          `nlsList' 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 `nls' 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 `nlsList' object.

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

     `nls', `nlme.nlsList'.

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

     data(CO2)
     fm1 <- nlsList(uptake ~ SSasympOff(conc, Asym, lrc, c0),
        data = CO2, start = c(Asym = 30, lrc = -4.5, c0 = 52))
     summary(fm1)

