ftrend                  package:Epi                  R Documentation

_F_i_t _a _f_l_o_a_t_i_n_g _t_r_e_n_d _t_o _a _f_a_c_t_o_r _i_n _g_e_n_e_r_a_l_i_z_e_d _l_i_n_e_a_r _m_o_d_e_l

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

     Fits a "floating trend" model to the given factor in a glm in a
     generalized linear model by centering covariates.

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

     ftrend(object, ...)

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

  object: fitted 'lm' or 'glm' object. The model must not have an
          intercept term

     ...: arguments to the 'nlm' function

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

     'ftrend()' calculates "floating trend" estimates for factors in
     generalized linear models.  This is an alternative to treatment
     contrasts suggested by Greenland et al. (1999).  If a regression
     model is fitted with no intercept term, then contrasts are  not
     used for the first factor in the model. Instead, there is one
     parameter  for each level of this factor.  However, the
     interpretation of these parameters,  and their variance-covariance
     matrix, depends on the numerical coding used for the  covariates.
     If an arbitrary constant is added to the covariate values, then  
     the variance matrix is changed. 

     The 'ftrend()' function takes the fitted model and works out an
     optimal  constant to add to the covariate values so that the
     covariance matrix is approximately diagonal.  The parameter
     estimates can then be treated as approximately independent, thus
     simplifying their presentation. This is particularly useful for
     graphical display of dose-response relationships (hence the name).

     Greenland et al. (1999) originally suggested centring the
     covariates so that their weighted mean, using the fitted weights
     from the model, is zero.  This heuristic criterion is improved
     upon by 'ftrend()' which uses the same  minimum information
     divergence criterion as used by Plummer (2003) for floating
     variance calculations. 'ftrend()' calls 'nlm()' to do the
     minimization and will pass optional arguments to control it.

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

     A list with the following components 

    coef: coefficients for model with adjusted covariates.

    vcov: Variance-covariance matrix of adjusted coefficients.

_N_o_t_e:

     The "floating trend" method is an alternative to the "floating
     absolute risk" method, which is implemented in the function
     'float()'.

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

     Martyn Plummer

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

     Greenland S, Michels KB, Robins JM, Poole C and Willet WC (1999)
     Presenting statistical uncertainty in trends and dose-response
     relations, _American Journal of Epidemiology_, *149*, 1077-1086.

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

     'float'

