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 the profile log-likelihood function for a fitted model of class '"nls"'. _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. This can be a numeric or character vector. By default, all non-linear parameters are profiled. maxpts: maximum number of points to be used for profiling each parameter. alphamax: highest 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): Of the original version, 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', '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: default level is too extreme pr1 <- profile(fm1, alpha = 0.05) # profiled values for the two parameters pr1$A pr1$lrc # see also example(plot.profile.nls)