xy_Obj           package:ffmanova           R Documentation(latin1)

_C_r_e_a_t_i_o_n _o_f _a _d_e_s_i_g_n-_w_i_t_h-_r_e_s_p_o_n_s_e_s _o_b_j_e_c_t

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

     The function takes an object created by 'x_Obj' as input and add
     response values. Further initial computations for prediction and
     testing is made.

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

     xy_Obj(xObj, Y)

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

    xObj: object created by 'x_Obj'

       Y: response matrix

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

     Traditionally, sums of squares and cross-products (SSC) is the
     multivariate generalisation of sums of squares. When there is a
     large number of responses this representation is inefficient and
     therefore linear combinations of observations (Langsrud, 2002) is
     stored instead, such as 'errorObs'. The corresponding SSC matrix
     can be obtained by 't(errorObs)%*%errorObs'. When there is a large
     number of observations the errorObs representation is also
     inefficient, but it these cases it is possible to chose a
     representation with several zero rows. Then, errorObs is stored as
     a two-component list: A matrix containing the nonzero rows of
     errorObs and an integer representing the degrees of freedom for
     error (number of rows in the full errorObs matrix).

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

     A list with components 

    xObj: same as input

       Y: same as input

ssTotFull: equals 'sum(Y^2)'

   ssTot: equals 'sum((center(Y))^2)'. That is, the total sum of
          squares summed over all responses.

      ss: Sums of squares summed over all responses.

    Beta: Output from 'linregEst' where 'xObj$D_om' is the regressor
          matrix.

    Yhat: fitted values

 YhatStd: standard deviations of fitted values

 msError: mean square error of each response

errorObs: Error observations that can be used in multivariate testing

  hypObs: Hypothesis observations that can be used in multivariate
          testing

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

     yvind Langsrud and Bjrn-Helge Mevik

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

     Langsrud, . (2002)  50-50 Multivariate Analysis of Variance for
     Collinear Responses.  _The Statistician_, *51*, 305-317.

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

     'x_Obj'.

