plot.profile              package:MASS              R Documentation

_P_l_o_t_t_i_g _F_u_n_c_t_i_o_n_s _f_o_r '_p_r_o_f_i_l_e' _O_b_j_e_c_t_s

_D_e_s_c_r_i_p_t_i_o_n:

     'plot' and 'pairs' methods for objects of class '"profile"'.

_U_s_a_g_e:

     ## S3 method for class 'profile':
     plot(x, ...)
     ## S3 method for class 'profile':
     pairs(x, colours = 2:3, ...)

_A_r_g_u_m_e_n_t_s:

       x: an object inheriting from class '"profile"'.

 colours: Colours to be used for the mean curves conditional on 'x' and
          'y' respectively.

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

_D_e_t_a_i_l_s:

     This is the main 'plot' method for objects created by
     'profile.glm'.  It can also be called on objects created by
     'profile.nls', but they have a specific method,
     'plot.profile.nls'.

     The 'pairs' method shows, for each pair of parameters x and y, two
     curves intersecting at the maximum likelihood estimate, which give
     the loci of the points at which the tangents to the contours of
     the bivariate profile likelihood become vertical and horizontal,
     respectively.  In the case of an exactly bivariate normal profile
     likelihood, these two curves would be straight lines giving the
     conditional means of y|x and x|y, and the contours would be
     exactly elliptical.

_A_u_t_h_o_r(_s):

     Originally, D. M. Bates and W. N. Venables.  (For S in 1996.)

_S_e_e _A_l_s_o:

     'profile.glm', 'profile.nls'.

_E_x_a_m_p_l_e_s:

     ## see ?profile.glm for an example using glm fits.

     ## a version of example(profile.nls) from R >= 2.8.0
     fm1 <- nls(demand ~ SSasympOrig(Time, A, lrc), data = BOD)
     pr1 <- profile(fm1, alpha = 0.1)
     MASS:::plot.profile(pr1)
     pairs(pr1) # a little odd since the parameters are highly correlated

     ## an example from ?nls
     x <- -(1:100)/10
     y <- 100 + 10 * exp(x / 2) + rnorm(x)/10
     nlmod <- nls(y ~  Const + A * exp(B * x), start=list(Const=100, A=10, B=1))
     pairs(profile(nlmod))