

SSmicmen {nls}                               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_:

     library( nls )
     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)

