plot.profile.nls            package:stats            R Documentation

_P_l_o_t _a _p_r_o_f_i_l_e._n_l_s _O_b_j_e_c_t

_D_e_s_c_r_i_p_t_i_o_n:

     Displays a series of plots of the profile t function and
     interpolated confidence intervals for the parameters in a
     nonlinear regression model that has been fit with 'nls' and
     profiled with 'profile.nls'.

_U_s_a_g_e:

     ## S3 method for class 'profile.nls':
     plot(x, levels, conf= c(99, 95, 90, 80, 50)/100,
          nseg = 50, absVal =TRUE, ...)

_A_r_g_u_m_e_n_t_s:

       x: an object of class '"profile.nls"' 

  levels: levels, on the scale of the absolute value of a t statistic,
          at which to interpolate intervals.  Usually 'conf' is used
          instead of giving 'levels' explicitly.

    conf: a numeric vector of confidence levels for profile-based
          confidence intervals on the parameters.  Defaults to 'c(0.99,
          0.95, 0.90, 0.80, 0.50).'

    nseg: an integer value giving the number of segments to use in the
          spline interpolation of the profile t curves. Defaults to 50.

  absVal: a logical value indicating whether or not the plots should be
          on the scale of the absolute value of the profile t. Defaults
          to 'TRUE'.

     ...: other arguments to the 'plot' function can be passed here.

_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', '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 )
     opar <- par(mfrow = c(2,2), oma = c(1.1, 0, 1.1, 0), las = 1)
     plot(pr1, conf = c(95, 90, 80, 50)/100)
     plot(pr1, conf = c(95, 90, 80, 50)/100, absVal = FALSE)
     mtext("Confidence intervals based on the profile sum of squares",
           side = 3, outer = TRUE)
     mtext("BOD data - confidence levels of 50%, 80%, 90% and 95%",
           side = 1, outer = TRUE)
     par(opar)

