AR                    package:drc                    R Documentation

_A_s_y_m_p_t_o_t_i_c _r_e_g_r_e_s_s_i_o_n _m_o_d_e_l

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

     Providing the mean function and the corresponding self starter
     function for the asymptotic regression model.

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

       AR.2(fixed = c(NA, NA), names = c("d", "e"), ...)

       AR.3(fixed = c(NA, NA, NA), names = c("c", "d", "e"), ...)

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

   fixed: numeric vector. Specifies which parameters are fixed and at
          what value they are fixed.  NAs for parameter that are not
          fixed.

   names: vector of character strings giving the names of the
          parameters (should not contain ":").

     ...: additional arguments to be passed from the convenience
          functions.

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

     The asymptotic regression model is a three-parameter model with
     mean function:


                    f(x) = c + (d-c)(1-exp(-x/e))


     The parameter c is the lower limit (at x=0), the parameter d is
     the upper limit  and the parameter e>0 is determining the
     steepness of the increase as x.

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

     A list of class 'drcMean', containing the mean function, the self
     starter function, the parameter names and other components such as
     derivatives and a function for calculating ED values.

_N_o_t_e:

     The functions are for use with the function 'drm'.

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

     Christian Ritz

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

     A very similar, but monotonously decreasing model is the
     exponential decay model: 'EXD.2' and 'EXD.3'.

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

     ## First model
     met.as.m1<-drm(gain~dose, product, data=methionine, fct=AR.3(), 
     pmodels = list(~1, ~factor(product), ~factor(product)))
     plot(met.as.m1, log = "", ylim=c(1450, 1800))
     summary(met.as.m1)

     ## Calculating bioefficacy: approach 1
     coef(met.as.m1)[5] / coef(met.as.m1)[4] * 100

     ## Calculating bioefficacy: approach 2
     SI(met.as.m1, c(50,50))

     ## Simplified models
     met.as.m2<-drm(gain~dose, product, data=methionine, fct=AR.3(), 
     pmodels = list(~1, ~1, ~factor(product)))
     anova(met.as.m2, met.as.m1)  # simplification not possible

     met.as.m3<-drm(gain~dose, product, data=methionine, fct=AR.3(), pmodels = list(~1, ~factor(product), ~1))
     anova(met.as.m3, met.as.m1)  # simplification not possible

