simDR                  package:drc                  R Documentation

_S_i_m_u_l_a_t_i_n_g _E_D _v_a_l_u_e_s _u_n_d_e_r _v_a_r_i_o_u_s _s_c_e_n_a_r_i_o_s

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

     Simulating ED values for a given model and given dose values.

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

       simDR(mpar, sigma, fct, noSim = 1000, conc, edVec = c(10, 50), seedVal = 20070723)

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

    mpar: numeric vector of model parameters

   sigma: numeric specifying the residual standard deviation

     fct: list supplying the chosen mean function

    conc: numeric vector of concentration/dose values

   edVec: numeric vector of ED values to estimate in each simulation

   noSim: numeric giving the number of simulations

 seedVal: numeric giving the seed used to initiate the random number
          generator

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

     The arguments 'mpar' and 'sigma' are typically obtained from a
     previous model fit.

     Only dose-response models assuming normally distributed errors can
     be used.

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

     A list of matrices with as many components as there are chosen ED
     values. The entries in the matrices are empirical standard
     deviations of the estimated ED values. Row-wise from top to bottom
     more and more concentration/dose values are included in the
     simulations; top row starting with 5 concentrations. The number of
     replicates increases column by column from left to right. 

     The list is returned invisbly as the matrices also are displayed.

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

     Christian Ritz

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

     ryegrass.m1 <- drm(ryegrass, fct=LL.4())

     simDR(coef(ryegrass.m1), sqrt(summary(ryegrass.m1)$resVar), LL.4(), 2, 
     c(1.88, 3.75, 7.50, 0.94, 15, 0.47, 30, 0.23, 60), seedVal = 200710291)

