Gauss3                package: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 `Gauss3' data frame has 250 rows and 2 columns giving
     generated data of Gaussian peaks with a decaying exponential
     background.

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

       y: A numeric vector of generated responses. 

       x: A numeric vector of generated inputs. 

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

     This data frame contains the following columns:

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

     The data are two strongly-blended Gaussians on a  decaying
     exponential baseline plus normally  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:

     data(Gauss3)
     plot(y ~ x, data = Gauss3)
     fm1 <- nls(y ~ b1*exp( -b2*x ) + b3*exp( -(x-b4)**2 / b5**2 )
                    + b6*exp( -(x-b7)**2 / b8**2 ), data = Gauss3, trace = TRUE,
                start = c(b1 = 94.9, b2 = 0.009, b3 = 90.1, b4 = 113, b5 = 20,
                          b6 = 73.8, b7 = 140, b8 = 20))
     fm2 <- nls(y ~ b1*exp( -b2*x ) + b3*exp( -(x-b4)**2 / b5**2 )
                    + b6*exp( -(x-b7)**2 / b8**2 ), data = Gauss3, trace = TRUE,
                start = c(b1 = 96, b2 = 0.0096, b3 = 80, b4 = 110, b5 = 25,
                          b6 = 74, b7 = 139, b8 = 25)) 
     fm3 <- nls(y ~ cbind(exp(-b2*x), exp(-(x-b4)**2/b5**2), exp(-(x-b7)**2/b8**2)),
                data = Gauss3, trace = TRUE,
                start = c(b2 = 0.009, b4 = 113, b5 = 20, b7 = 140, b8 = 20),
                algorithm = "plinear")
     fm4 <- nls(y ~ cbind(exp(-b2*x), exp(-(x-b4)**2/b5**2), exp(-(x-b7)**2/b8**2)),
                data = Gauss3, trace = TRUE,
                start = c(b2 = 0.0096, b4 = 110, b5 = 25, b7 = 139, b8 = 25),
                algorithm = "plinear")

