profile.nls              package:stats              R Documentation

_M_e_t_h_o_d _f_o_r _P_r_o_f_i_l_i_n_g _n_l_s _O_b_j_e_c_t_s

_D_e_s_c_r_i_p_t_i_o_n:

     Investigates behavior of the log-likelihood function near the
     solution represented by 'fitted'.

_U_s_a_g_e:

     ## S3 method for class 'nls':
     profile(fitted, which = 1:npar, maxpts = 100, alphamax = 0.01,
             delta.t = cutoff/5, ...)

_A_r_g_u_m_e_n_t_s:

  fitted: the original fitted model object.

   which: the original model parameters which should be profiled. By
          default, all parameters are profiled.

  maxpts: maximum number of points to be used for profiling each
          parameter.

alphamax: maximum significance level allowed for the profile
          t-statistics.

 delta.t: suggested change on the scale of the profile t-statistics.
          Default value chosen to allow profiling at about 10 parameter
          values.

     ...: further arguments passed to or from other methods.

_D_e_t_a_i_l_s:

     The profile t-statistics is defined as the square root of change
     in sum-of-squares divided by residual standard error with an
     appropriate sign.

_V_a_l_u_e:

     A list with an element for each parameter being profiled. The
     elements are data-frames with two variables  

par.vals: a matrix of parameter values for each fitted model.

     tau: The profile t-statistics.

_A_u_t_h_o_r(_s):

     Douglas M. 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 (chapter 6)

_S_e_e _A_l_s_o:

     'nls', 'profile', 'profiler.nls', 'plot.profile.nls'

_E_x_a_m_p_l_e_s:

     # obtain the fitted object
     fm1 <- nls(demand ~ SSasympOrig( Time, A, lrc ), data = BOD)
     # get the profile for the fitted model
     pr1 <- profile( fm1 )
     # profiled values for the two parameters
     pr1$A
     pr1$lrc

