maED                   package:drc                   R Documentation

_E_s_t_i_m_a_t_i_o_n _o_f _E_D _v_a_l_u_e_s _u_s_i_n_g _m_o_d_e_l-_a_v_e_r_a_g_i_n_g

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

     Estimates and confidence intervals for ED values are estimated
     using model-averaging.

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

     maED(object, fctList = NULL, respLev, interval = c("none", "buckland", "kang"), 
     level = 0.95, display = TRUE, na.rm = FALSE, extended = FALSE)

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

  object: an object of class 'drc'.

 fctList: a list of non-linear functions to be compared.

 respLev: a numeric vector containing the response levels.

interval: character string specifying the type of confidence intervals
          to be supplied. The default is "none".  The choices
          "buckland" and "kang" are explained in the Details section.

   level: numeric. The level for the confidence intervals. The default
          is 0.95.

 display: logical. If TRUE results are displayed. Otherwise they are
          not (useful in simulations).

   na.rm: logical indicating whether or not NA occurring during model
          fitting should be left out of subsequent calculations.

extended: logical specifying whether or not an extended output
          (including fit summaries) should be returned.

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

     Model-averaging of individual estimates is carried out as
     described by Buckland _et al._ (1997) and  Kang _et al._ (2000)
     using AIC-based weights. The two approaches differ w.r.t. the
     calculation of confidence intervals: Buckland _et al._ (1997)
     provide an approximate variance formula under the assumption of
     perfectly correlated estimates (so, confidence intervals will tend
     to be too wide),  whereas Kang _et al._ (2000) use the model
     weights to calculate confidence limits as weighted means of  the
     confidence limits for the individual fits.

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

     A matrix with two or more columns, containing the estimates  and
     the corresponding estimated standard errors and possibly lower and
     upper confidence limits.

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

     Christian Ritz

_R_e_f_e_r_e_n_c_e_s:

     Buckland, S. T. and Burnham, K. P. and Augustin, N. H. (1997)
     Model Selection: An Integral Part of Inference _Biometrics_ *53*,
     603-618.

     Kang, Seung-Ho and Kodell, Ralph L. and Chen, James J. (2000)
     Incorporating Model Uncertainties along with Data Uncertainties in
     Microbial Risk Assessment, _Regulatory Toxicology and
     Pharmacology_ *32*, 68-72.

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

     The function 'mselect' provides a summary of fit statistics for
     several models fitted to the same data.

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

     ryegrass.m1 <- drm(rootl~conc, data = ryegrass, fct = LL.4())
       
     mselect(ryegrass.m1, list(LL.5(), LN.4(), W1.4(), W2.4(), FPL.4(-1,1), FPL.4(-2,3), FPL.4(-0.5,0.5)))
       
     maED(ryegrass.m1, list(LL.5(), LN.4(), W1.4(), W2.4(), FPL.4(-1,1), FPL.4(-2,3), FPL.4(-0.5,0.5)), 
     c(10, 50, 90))  
       
     maED(ryegrass.m1, list(LL.5(), LN.4(), W1.4(), W2.4(), FPL.4(-1,1), FPL.4(-2,3), FPL.4(-0.5,0.5)), 
     c(10, 50, 90), "buckland")

     maED(ryegrass.m1, list(LL.5(), LN.4(), W1.4(), W2.4(), FPL.4(-1,1), FPL.4(-2,3), FPL.4(-0.5,0.5)), 
     c(10, 50, 90), "kang")

