nls                  package:FLCore                  R Documentation

_M_e_t_h_o_d _n_l_s

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

     For a given forumla (describing a model) and data this method
     applies the simple non- linear least squares algorithm - this
     calculates the parameters that minimisethe sum of  squares
     difference between the observed (data) and predicted (model)
     values.

     The algorithm can be sensitive to the initial values of the
     problem so do try different  start points and check they converge
     to the same estimates.

_G_e_n_e_r_i_c _f_u_n_c_t_i_o_n:


     nls(formula,data,start,control,algorithm,trace,subset,weights,na.action,model,lower,upper)

_M_e_t_h_o_d_s:


      _s_i_g_n_a_t_u_r_e(_f_o_r_m_u_l_a=_F_L_M_o_d_e_l,_d_a_t_a=_m_i_s_s_i_n_g,_s_t_a_r_t=_m_i_s_s_i_n_g,_c_o_n_t_r_o_l=_m_i_s_s_i_n_g,_a_l_g_o_r_i_t_h_m=_m_i_s_s_i_n_g,_t_r_a_c_e=_m_i_s_s_i_n_g,_s_u_b_s_e_t=_m_i_s_s_i_n_g,_w_e_i_g_h_t_s=_m_i_s_s_i_n_g,_n_a._a_c_t_i_o_n=_m_i_s_s_i_n_g,_m_o_d_e_l=_m_i_s_s_i_n_g,_l_o_w_e_r=_m_i_s_s_i_n_g,_u_p_p_e_r=_m_i_s_s_i_n_g) : 
          Applies non-linear sum of squares to the model and data in
          the input FLModel-class object.

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

     The FLR Team

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

     FLComp

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

     # An example FLSR (FLModel) object
     data(nsher)

     #set bevholt model
     model(nsher) <- bevholt

     # fit through nls
     nsher <- nls(nsher)

     summary(nsher)

