mselect                 package:drc                 R Documentation

_M_o_d_e_l _s_e_l_e_c_t_i_o_n

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

     Model selection by comparison of different models using the
     maximum log likelihood value,  Akaike's information criterion
     (AIC), the estimated residual variance  and the p-value from a
     lack-of-fit test as criteria.

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

       mselect(object, fctList = NULL, nested = 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.

  nested: logical. TRUE results in F tests between adjacent models (in
          'fctList').  Only sensible for nested models.

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

     The maximum likelihood cannot be used for comparison unless the
     models are nested.

     For Akaike's information criterion and the residual variance: the
     smaller the better and  for lack-of-fit test: the larger the
     better. Note that the residual variance is only available for 
     continuous dose-response data.

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

     A matrix with one row for each model and one column for each
     criterion.

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

     Christian Ritz

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

     ### Example with continuous data
     ## Fitting initial four-parameter log-logistic model
     ryegrass.m1 <- drm(rootl ~ conc, data = ryegrass, fct = LL.4())

     ## Model selection
     mselect(ryegrass.m1, list(LL.3(), LL.5(), W1.3(), W1.4(), W2.4(), baro5()))

     mselect(ryegrass.m1, list(LL.5()), nested = TRUE)  # nested models

     ### Example with quantal data
     ## Fitting initial two-parameter log-logistic model
     earthworms.m1 <- drm(number/total~dose, weights=total, 
     data = earthworms, fct = LL.2(), type = "binomial")

     ## Comparing 4 models
     mselect(earthworms.m1, list(W1.2(), W2.2(), LL.3()))
     # model selection based AIC or p value not working in this example!!!

