parm                package:multcomp                R Documentation

_M_o_d_e_l _P_a_r_a_m_e_t_e_r_s

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

     Directly specify estimated model parameters and their covariance
     matrix.

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

     parm(coef, vcov, df = 0)

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

    coef: estimated coefficients.

    vcov: estimated covariance matrix of the coefficients.

      df: an optional specification of the degrees of freedom to be
          used in subsequent computations. 

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

     When only estimated model parameters and the corresponding
     covariance matrix is available for simultaneous inference using
     'glht' (for example, when only the results but not the original
     data are available or, even worse, when the model has been fitted
     outside R), function 'parm' sets up an object 'glht' is able to
     compute on (mainly  by offering 'coef' and 'vcov' methods).

     Note that the linear function in 'glht' can't  be specified via
     'mcp' since the model terms are missing.

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

     An object of class 'parm' with elements 

    coef: model parameters

    vcov: covariance matrix of model parameters

      df: degrees of freedom

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

     ## example from
     ## Bretz, Hothorn, and Westfall (2002). 
     ## On multiple comparisons in R. R News, 2(3):14-17.

     beta <- c(V1 = 14.8, V2 = 12.6667, V3 = 7.3333, V4 = 13.1333)
     Sigma <- 6.7099 * (diag(1 / c(20, 3, 3, 15)))
     confint(glht(model = parm(beta, Sigma, 37),
                  linfct = c("V2 - V1 >= 0", 
                             "V3 - V1 >= 0", 
                             "V4 - V1 >= 0")), 
             level = 0.9)

