nls.control              package:stats              R Documentation

_C_o_n_t_r_o_l _t_h_e _I_t_e_r_a_t_i_o_n_s _i_n _n_l_s

_D_e_s_c_r_i_p_t_i_o_n:

     Allow the user to set some characteristics of the 'nls' nonlinear
     least squares algorithm.

_U_s_a_g_e:

     nls.control(maxiter = 50, tol = 1e-05, minFactor = 1/1024,
                 printEval = FALSE, warnOnly = FALSE)

_A_r_g_u_m_e_n_t_s:

 maxiter: A positive integer specifying the maximum number of
          iterations allowed.

     tol: A positive numeric value specifying the tolerance level for
          the relative offset convergence criterion.

minFactor: A positive numeric value specifying the minimum step-size
          factor allowed on any step in the iteration.  The increment
          is calculated with a Gauss-Newton algorithm and successively
          halved until the residual sum of squares has been decreased
          or until the step-size factor has been reduced below this
          limit.

printEval: a logical specifying whether the number of evaluations
          (steps in the gradient direction taken each iteration) is
          printed.

warnOnly: a logical specifying whether 'nls()' should return instead of
          signalling an error in the case of termination before
          convergence. Termination before convergence happens upon
          completion of 'maxiter' iterations, in the case of a singular
          gradient, and in the case that the step-size factor is
          reduced below 'minFactor'.

_V_a_l_u_e:

     A 'list' with exactly five components: 

 maxiter: 

     tol: 

minFactor: 

printEval: 

warnOnly: 

     with meanings as explained under 'Arguments'.

_A_u_t_h_o_r(_s):

     Douglas Bates and Saikat DebRoy

_R_e_f_e_r_e_n_c_e_s:

     Bates, D. M. and Watts, D. G. (1988), _Nonlinear Regression
     Analysis and Its Applications_, Wiley.

_S_e_e _A_l_s_o:

     'nls'

_E_x_a_m_p_l_e_s:

     nls.control(minFactor = 1/2048)