IndomethODE             package:nlmeODE             R Documentation

_P_h_a_r_m_a_c_o_k_i_n_e_t_i_c _m_o_d_e_l_l_i_n_g _o_f _I_n_d_o_m_e_t_h_a_c_i_n _u_s_i_n_g _d_i_f_f_e_r_e_n_t_i_a_l _e_q_u_a_t_i_o_n_s

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

     The 'Indometh' data frame has 66 rows and 3 columns of data on the
     pharmacokinetics of indomethacin.

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

     The 'Indometh' data frame contains the following columns:

     '_S_u_b_j_e_c_t' an ordered factor with containing the subject codes. 
          The ordering is according to increasing maximum response.

     '_t_i_m_e' a numeric vector of times at which blood samples were drawn
          (hr).

     '_c_o_n_c' a numeric vector of plasma concentrations of indomethacin
          (mcg/ml).

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

     Each of the six subjects were given an intravenous injection of
     indomethacin.

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

     Kwan, Breault, Umbenhauer, McMahon and Duggan (1976), ``Kinetics
     of Indomethacin absorption, elimination, and enterohepatic
     circulation in man'', Journal of Pharmacokinetics and
     Biopharmaceutics, 4, 255-280.

     Davidian, M. and Giltinan, D. M. (1995) Nonlinear Models for
     Repeated Measurement Data, Chapman & Hall (section 5.2.4, p. 134)

     Pinheiro, J. C. and Bates, D. M. (2000) Mixed-effects Models in S
     and S-PLUS, Springer.

_S_e_e _A_l_s_o:

     'Indometh', 'SSbiexp'

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

     data(Indometh)

     TwoComp <- list(DiffEq=list(                         
                         dy1dt = ~ -(k12+k10)*y1+k21*y2 , 
                         dy2dt = ~ -k21*y2 + k12*y1),     
                     ObsEq=list(                          
                         c1 = ~ y1,                       
                         c2 = ~ 0),                       
                     States=c("y1","y2"),                 
                     Parms=c("k12","k21","k10","start"),  
                     Init=list("start",0))

     IndomethModel <- nlmeODE(TwoComp,Indometh)

     #Remove '#' below to run the estimation

     #Indometh.nlme <- nlme(conc ~ IndomethModel(k12,k21,k10,start,time,Subject),
     #   data = Indometh, fixed=k12+k21+k10+start~1, random = pdDiag(start+k12+k10~1), 
     #   start=c(k12=-0.05,k21=-0.15,k10=-0.10,start=0.70),
     #   control=list(msVerbose=TRUE,tolerance=1e-1,pnlsTol=1e-1,msTol=1e-1),
     #   verbose=TRUE)

     #plot(augPred(Indometh.nlme,level=0:1))

