MGH10                package:NISTnls                R Documentation

_M_o_r_e, _G_a_b_r_o_w _a_n_d _H_i_l_l_s_t_r_o_m _e_x_a_m_p_l_e _1_0

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

     The `MGH10' data frame has 16 rows and 2 columns.

_A_r_g_u_m_e_n_t_s:

       y: A numeric vector of response values. 

       x: A numeric vector of input values. 

_F_o_r_m_a_t:

     This data frame contains the following columns:

_D_e_t_a_i_l_s:

     This problem was found to be difficult for some very good
     algorithms.

     See More, J. J., Garbow, B. S., and Hillstrom, K. E.  (1981). 
     Testing unconstrained optimization software. ACM Transactions on
     Mathematical Software. 7(1):  pp. 17-41.

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

_S_o_u_r_c_e:

     Meyer, R. R. (1970).   Theoretical and computational aspects of
     nonlinear  regression.  In Nonlinear Programming, Rosen, 
     Mangasarian and Ritter (Eds).   New York, NY: Academic Press, pp.
     465-486.

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

     data(MGH10)
     plot(y ~ x, data = MGH10)
     ## check plot on log scale for shape
     plot(y ~ x, data = MGH10, log = "y")

     ## starting values for this run are ridiculous
     fm1 <- nls(y ~ b1 * exp(b2/(x+b3)), data = MGH10, trace = TRUE,
                start = c(b1 = 2, b2 = 400000, b3 = 25000))

     fm2 <- nls(y ~ b1 * exp(b2/(x+b3)), data = MGH10, trace = TRUE,
                start = c(b1 = 0.02, b2 = 4000, b3 = 250))

     fm3 <- nls(y ~ exp(b2/(x+b3)), data = MGH10, trace = TRUE,
                start = c(b2 = 400000, b3 = 25000),
                algorithm = "plinear")

     fm4 <- nls(y ~ exp(b2/(x+b3)), data = MGH10, trace = TRUE,
                start = c(b2 = 4000, b3 = 250),
                algorithm = "plinear")

