eval.design            package:AlgDesign            R Documentation

_E_v_a_l_u_a_t_e_s _a _d_e_s_i_g_n.

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

     A design is evaluated.

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

     eval.design(frml,design,confounding=FALSE,variances=TRUE,center=FALSE,X=NULL)

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

    frml: The formula used to create the design.

  design: The design, which may be the design part of the output of
          optFederov().

confounding: If confounding=TRUE, the confounding patterns will be
          shown.

variances: If TRUE, the variances each term will be output.

  center: If TRUE, numeric variables will be centered before frml is
          applied.

       X: X is either the matrix describing the prediction space for I
          or for G, the  the candidate set from which the design was
          chosen. They are often the same.

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

confounding: A matrix. The columns of which give the regression
          coefficients of each variable regressed on the others. If C
          is the confounding matrix, then  -ZC is a matrix of residuals
          of the variables regressed on the other variables.

determinant: (det(M/N)^(1/k), where M=Z'Z/N, and Z is  the model
          expanded N x k design matrix.

       A: The average coefficient variance: trace(Mi)/k, where Mi is
          the  inverse of M.

       I: The average prediction variance over X, which can be shown to
          be  trace((X'X*Mi)/N.

      Ge: The minimax normalized variance over X, expressed as an
          efficiency with respect  to the optimal approximate theory
          design. It is defined as k/max(d), where  max(d) is the
          maximum normalized variance over X - i.e. the max of 
          x'(Mi)x, over all rows x' of X.

     Dea: A lower bound on 'D' efficiency for approximate theory
          designs. It is  equal to exp(1-1/Ge).

diagonality: The diagonality of the design, excluding the constant, if
          any. Diagonality is defined as (|M1|/prod(diag(M1)))^(1/k),
          where  M1 is M with first column and row deleted when there
          is a constant.

gmean.variances: The geometric mean of the coefficient variances.

_N_o_t_e:

     I, Ge and Dea are calculated only when X is input.

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

     Bob Wheeler bwheeler@echip.com

     Please cite this program as follows:

     Wheeler, R.E. (2004). eval.design. _AlgDesign_. The R project for
     statistical computing <URL: http://www.r-project.org/>

