

MGH17(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_7

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

     The `MGH17' data frame has 33 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.

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

     Osborne, M. R. (1972).  Some aspects of nonlinear least
     squares calculations.  In Numerical Methods for Nonlin-
     ear Optimization, Lootsma (Ed).  New York, NY:  Aca-
     demic Press, pp. 171-189.

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

     library(NISTnls)
     data(MGH17)
     plot(y ~ x, data = MGH17)
     ## Another set of absurd starting values
     #fm1 <- nls(y ~ b1 + b2*exp(-x*b4) + b3*exp(-x*b5),
     #           data = MGH17, trace = TRUE,
     #           start = c(b1 = 50, b2 = 150, b3 = -100, b4 = 1, b5 = 2),
     #           warnOnly = TRUE)
     fm2 <- nls(y ~ b1 + b2*exp(-x*b4) + b3*exp(-x*b5),
                data = MGH17, trace = TRUE,
                start = c(b1 = 0.5, b2 = 1.5, b3 = -1, b4 = 0.01, b5 = 0.02),
                warnOnly = TRUE)
     #fm3 <- nls(y ~ cbind(1, exp(-x*b4), exp(-x*b5)),
     #           data = MGH17, trace = TRUE, algorithm = "plinear",
     #           start = c(b4 = 1, b5 = 2),
     #           warnOnly = TRUE)
     fm4 <- nls(y ~ cbind(1, exp(-x*b4), exp(-x*b5)),
                data = MGH17, trace = TRUE, algorithm = "plinear",
                start = c(b4 = 0.01, b5 = 0.02),
                warnOnly = TRUE)

