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.

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

     This data frame contains the following columns:

     _y A numeric vector of response values.

     _x A numeric vector of input values.

_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")
     ## Don't run: 
     ## 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))
     ## End Don't run
     fm2 <- nls(y ~ b1 * exp(b2/(x+b3)), data = MGH10, trace = TRUE,
                start = c(b1 = 0.02, b2 = 4000, b3 = 250))
     ## Don't run: 
     fm3 <- nls(y ~ exp(b2/(x+b3)), data = MGH10, trace = TRUE,
                start = c(b2 = 400000, b3 = 25000),
                algorithm = "plinear")
     ## End Don't run
     fm4 <- nls(y ~ exp(b2/(x+b3)), data = MGH10, trace = TRUE,
                start = c(b2 = 4000, b3 = 250),
                algorithm = "plinear")

