SSmicmen                package:stats                R Documentation

_M_i_c_h_a_e_l_i_s-_M_e_n_t_e_n _M_o_d_e_l

_D_e_s_c_r_i_p_t_i_o_n:

     This 'selfStart' model evaluates the Michaelis-Menten model and
     its gradient.  It has an 'initial' attribute that will evaluate
     initial estimates of the parameters 'Vm' and 'K'

_U_s_a_g_e:

     SSmicmen(input, Vm, K)

_A_r_g_u_m_e_n_t_s:

   input: a numeric vector of values at which to evaluate the model.

      Vm: a numeric parameter representing the maximum value of the
          response.

       K: a numeric parameter representing the 'input' value at which
          half the maximum response is attained.  In the field of
          enzyme kinetics this is called the Michaelis parameter.

_V_a_l_u_e:

     a numeric vector of the same length as 'input'.  It is the value
     of the expression 'Vm*input/(K+input)'.  If both the arguments
     'Vm' and 'K' are names of objects, the gradient matrix with
     respect to these names is attached as an attribute named
     'gradient'.

_A_u_t_h_o_r(_s):

     Jose Pinheiro and Douglas Bates

_S_e_e _A_l_s_o:

     'nls', 'selfStart'

_E_x_a_m_p_l_e_s:

     data( Puromycin )
     PurTrt <- Puromycin[ Puromycin$state == "treated", ]
     SSmicmen( PurTrt$conc, 200, 0.05 )  # response only
     Vm <- 200; K <- 0.05
     SSmicmen( PurTrt$conc, Vm, K ) # response and gradient
     getInitial(rate ~ SSmicmen(conc, Vm, K), data = PurTrt)
     ## Initial values are in fact the converged values
     fm1 <- nls(rate ~ SSmicmen(conc, Vm, K), data = PurTrt)
     summary( fm1 )
     ## Alternative call using the subset argument
     fm2 <- nls(rate ~ SSmicmen(conc, Vm, K), data = Puromycin,
                subset = state == "treated")
     summary(fm2)

