biexp                   package:PK                   R Documentation

_T_w_o-_P_h_a_s_e _H_a_l_f-_L_i_f_e _E_s_t_i_m_a_t_i_o_n _b_y _B_i_e_x_p_o_n_e_n_t_i_a_l _M_o_d_e_l

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

     Estimation of inital and terminal half-life by fitting a
     biexponential model.

_U_s_a_g_e:

     biexp(conc, time, prev=0, tol=1E-9, maxit=500)

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

    time: time points of concentration assessments. 

    conc: levels of concentrations. 

    prev: pre-dosing value. 

     tol: relative error tolerance. 

   maxit: maximum number of iterations.

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

     Estimation of inital and terminal half-life using the
     biexponential 'y=a1*exp(-b1*x)+a2*exp(-b2*x)' model with a
     parametrization to ensure b1 > b2 > 0 fitted by the least squares
     criteria with function 'optim' of package 'base' with 'method'
     "Nelder-Mead". Curve peeling (Foss, 1969) is used get start values
     for nonlinear model fitting. When no adequate starting values are
     determined by curve peeling, a single exponential model is fitted
     with starting values obtained from an OLS regression on log
     transformed values with a parametrization to ensure a slope > 0. 


     If the pre-dosing value indicating the intrinsic level is greater
     than 0, the pre-dosing value is subtracted from all concentration
     levels before calculation of initial and terminal half-life.

_V_a_l_u_e:

     A list of S3 class '"halflife"' containing the following
     components: 


   parms: half-life and model estimates.

    time: time points of concentration assessments. 

    conc: levels of concentrations. 

  method: "biexp". 

_N_o_t_e:

     Records including missing values and values below or equal to zero
     are omitted.

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

     Martin J. Wolfsegger and Thomas Jaki

_R_e_f_e_r_e_n_c_e_s:

     Foss S. D. (1969). A Method for Obtaining Initial Estimates of the
     Parameters in Exponential Curve Fitting. Biometrics. 25:580-584 

      Pinheiro J. C. and Bates D. M. (200). Mixed-Effects Models in S
     and S-PLUS. Springer, New York. 


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

     ## examples from Pinheiro J.C. and Bates D.M. (2000, page 279) 
     time <- c(0.25, 0.5, 0.75, 1, 1.25, 2, 3, 4, 5, 6, 8, 0.25, 0.5, 0.75, 1, 1.25, 
     2, 3, 4, 5, 6, 8, 0.25, 0.5, 0.75, 1, 1.25, 2, 3, 4, 5, 6, 8, 0.25, 0.5, 0.75, 1, 
     1.25, 2, 3, 4, 5, 6, 8, 0.25, 0.5, 0.75, 1, 1.25, 2, 3, 4, 5, 6, 8, 0.25, 0.5, 
     0.75, 1, 1.25, 2, 3, 4, 5, 6, 8)

     conc <- c(1.5, 0.94, 0.78, 0.48, 0.37, 0.19, 0.12, 0.11, 0.08, 0.07, 0.05, 2.03, 
     1.63, 0.71, 0.7, 0.64, 0.36, 0.32, 0.2, 0.25, 0.12, 0.08, 2.72, 1.49, 1.16, 0.8, 
     0.8, 0.39, 0.22, 0.12, 0.11, 0.08, 0.08, 1.85, 1.39, 1.02, 0.89, 0.59, 0.4, 0.16, 
     0.11, 0.1, 0.07, 0.07, 2.05, 1.04, 0.81, 0.39, 0.3, 0.23, 0.13, 0.11, 0.08, 0.1, 
     0.06, 2.31, 1.44, 1.03, 0.84, 0.64, 0.42, 0.24, 0.17, 0.13, 0.1, 0.09)

     result <- biexp(conc=conc, time=time)
     print(result)
     plot(result)

