

Gauss1(NISTnls)                              R Documentation

_G_e_n_e_r_a_t_e_d _d_a_t_a

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

     The `Gauss1' data frame has 250 rows and 2 columns of
     generated data.

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

       y: A numeric vector of generated responses.

       x: A numeric vector of generated input values.

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

     This data frame contains the following columns:

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

     The data are generated data with two well-separated
     Gaussians on a decaying exponential baseline plus nor-
     mally distributed zero-mean noise with variance = 6.25.

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

     Rust, B., NIST (1996).

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

     library(NISTnls)
     data(Gauss1)
     plot(y ~ x, data = Gauss1)
     fm1 <- nls(y ~ b1*exp( -b2*x ) + b3*exp( -(x-b4)**2 / b5**2 )
                    + b6*exp( -(x-b7)**2 / b8**2 ), data = Gauss1, trace = TRUE,
                start = c(b1 = 97.0, b2 = 0.009, b3 = 100.0, b4 = 65.0, b5 = 20.0,
                          b6 = 70.0, b7 = 178., b8 = 16.5))
     fm2 <- nls(y ~ b1*exp( -b2*x ) + b3*exp( -(x-b4)**2 / b5**2 )
                    + b6*exp( -(x-b7)**2 / b8**2 ), data = Gauss1, trace = TRUE,
                start = c(b1 = 94.0, b2 =  0.0105, b3 = 99.0, b4 = 63.0, b5 = 25.0,
                          b6 = 71.0, b7 = 180.0, b8 = 20.0))
     fm3 <- nls(y ~ cbind(exp(-b2*x), exp(-(x-b4)**2/b5**2), exp(-(x-b7)**2/b8**2)),
                data = Gauss1, trace = TRUE,
                start = c( b2 = 0.009, b4 = 65.0, b5 = 20.0, b7 = 178., b8 = 16.5),
                algorithm = "plinear")
     fm4 <- nls(y ~ cbind(exp(-b2*x), exp(-(x-b4)**2/b5**2), exp(-(x-b7)**2/b8**2)),
                data = Gauss1, trace = TRUE,
                start = c( b2 = 0.0105, b4 = 63.0, b5 = 25.0, b7 = 180., b8 = 20.0),
                algorithm = "plinear")

