drmc                   package:drc                   R Documentation

_S_e_t_s _c_o_n_t_r_o_l _a_r_g_u_m_e_n_t_s

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

     Set control arguments in the control argument in the function
     'drm'.

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

       drmc(constr = FALSE, errorm = TRUE, maxIt = 500, method="BFGS", 
       noMessage = FALSE, relTol = 1e-07, rmNA=FALSE, useD = FALSE, 
       trace = FALSE, otrace = FALSE, warnVal = -1)

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

  constr: logical. If TRUE optimisation is constrained, only yielding
          non-negative parameters.

  errorm: logical specifying whether failed convergence in 'drm' should
          result  in an error or only a warning.

   maxIt: numeric. The maximum number of iterations in the optimisation
          procedure.

  method: character string. The method used in the optimisation
          procedure.  See 'optim' for available methods.

noMessage: logical, specifying whether or not messages should be
          displayed.

  relTol: numeric. The relative tolerance in the optimisation
          procedure.

    rmNA: logical. Should NAs be removed from sum of squares used for
          estimation?  Default is FALSE (not removed).

    useD: logical. If TRUE derivatives are used for estimation (if
          available).

   trace: logical. If TRUE the trace from 'optim' is displayed.

  otrace: logical. If TRUE the output from 'optim' is displayed.

 warnVal: numeric. If equal to 0 then the warnings are stored and
          displayed at the end.  See under 'warn' in 'options'. The
          default results in suppression of warnings.

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

     A list with 8 components, one for each of the above arguments.

_N_o_t_e:

     The use of a non-zero constant 'bcAdd' may in some cases  make it
     more difficult to obtain convergence of the estimation procedure.

_A_u_t_h_o_r(_s):

     Christian Ritz

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

     ### Displaying the default settings
     drmc()  

     ### Using 'method' argument
     model1 <- drm(ryegrass, fct = LL.4())

     model2 <- drm(ryegrass, fct = LL.4(),
     control = drmc(method = "Nelder-Mead"))

