

rmaov(growth)                                R Documentation

_S_p_l_i_t_-_p_l_o_t _A_N_O_V_A _M_o_d_e_l

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

     `rmaov' performs the classical balanced split-plot
     ANOVA, with `summary' providing the table. This is the
     so-called repeated measures ANOVA.

     For unbalanced data, `elliptic' will perform the analy-
     sis for one or two levels of nesting.

_U_s_a_g_e_:

     rmaov(response, tvcov=NULL, ccov=NULL, analysis=T)

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

response: A matrix or dataframe of response values with
          units as rows and repeated measures as columns.

   tvcov: A numeric vector or factor variable defining the
          clusters. If there are several levels of nesting,
          a matrix or dataframe with columns of such vari-
          ables defining the nested clusters starting with
          the highest level (that is, from slowest to
          fastest varying). If not provided, each response
          value of a unit is assumed to belong to a differ-
          ent cluster (that is, one factor with
          `ncol(response)' levels is assumed).

    ccov: A vector or factor variable for one inter-subject
          covariate or a matrix, dataframe, or list of sev-
          eral such variables.

analysis: If FALSE, the design matrix is set up, but the
          analysis is not performed.

_V_a_l_u_e_:

     The fitted model is returned.

_A_u_t_h_o_r_(_s_)_:

     Ralf Goertz (ralf.goertz@uni-jena.de)

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

     `carma', `elliptic', `lm', `potthoff'.

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

     # vision data for 7 individuals, with response a 7x8 matrix
     # two levels of nesting: 4 levels of power for each eye
     y <- matrix(rnorm(56),ncol=8)
     tvc <- data.frame(eye=c(rep(1,4),rep(2,4)),power=c(1:4,1:4))
     summary(rmaov(y, tvc))

