proj                  package:base                  R Documentation

_P_r_o_j_e_c_t_i_o_n_s _o_f _M_o_d_e_l_s

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

     `proj' returns a matrix or list of matrices giving the projections
     of the data onto the terms of a linear model.  It is most
     frequently used for `aov' models.

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

     proj     (object, ...)  
     proj.aov    (object, onedf = FALSE, unweighted.scale = FALSE)  
     proj.aovlist(object, onedf = FALSE, unweighted.scale = FALSE)  
     proj.default(object, onedf = TRUE, ...)  
     proj.lm     (object, onedf = FALSE, unweighted.scale = FALSE)  

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

  object: An object of class `"lm"' or a class inheriting from it, or
          an object with a similar structure including in particular
          components `qr' and `effects'.

   onedf: A logical flag. If `TRUE', a projection is returned for all
          the columns of the model matrix. If `FALSE', the
          single-column projections are collapsed by terms of the model
          (as represented in the analysis of variance table).

unweighted.scale: If the fit producing `object' used weights, this
          determines if the projections correspond to weighted or
          unweighted observations.

     ...: Swallow and ignore any other arguments.

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

     A projection is given for each stratum of the object, so for `aov'
     models with an `Error' term the result is a list of projections.

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

     A projection matrix or (for multi-stratum objects) a list of
     projection matrices.

     Each projection is a matrix with a row for each observations and
     either a column for each term (`onedf = FALSE') or for each
     coefficient (`onedf = TRUE'). Projection matrices from the default
     method have orthogonal columns representing the projection of the
     response onto the column space of the Q matrix from the QR
     decomposition.  The fitted values are the sum of the projections,
     and the sum of squares for each column is the reduction in sum of
     squares from fitting that column (after those to the left of it).

     The methods for `lm' and `aov' models add a column to the
     projection matrix giving the residuals (the projection of the data
     onto the orthogonal complement of the model space).

     Strictly, when `onedf = FALSE' the result is not a projection, but
     the columns represent sums of projections onto the columns of the
     model matrix corresponding to that term. In this case the matrix
     does not depend on the coding used.

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

     B.D. Ripley

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

     `aov', `lm', `model.tables'

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

     N <- c(0,1,0,1,1,1,0,0,0,1,1,0,1,1,0,0,1,0,1,0,1,1,0,0)
     P <- c(1,1,0,0,0,1,0,1,1,1,0,0,0,1,0,1,1,0,0,1,0,1,1,0)
     K <- c(1,0,0,1,0,1,1,0,0,1,0,1,0,1,1,0,0,0,1,1,1,0,1,0)
     yield <- c(49.5,62.8,46.8,57.0,59.8,58.5,55.5,56.0,62.8,55.8,69.5,
     55.0, 62.0,48.8,45.5,44.2,52.0,51.5,49.8,48.8,57.2,59.0,53.2,56.0)

     npk <- data.frame(block=gl(6,4), N=factor(N), P=factor(P),
                       K=factor(K), yield=yield)
     npk.aov <- aov(yield ~ block + N*P*K, npk)
     proj(npk.aov)

     ## as a test, not particularly sensible
     options(contrasts=c("contr.helmert", "contr.treatment"))
     npk.aovE <- aov(yield ~  N*P*K + Error(block), npk)
     proj(npk.aovE)

