corCAR1                 package:nlme                 R Documentation

_C_o_n_t_i_n_u_o_u_s _A_R(_1) _C_o_r_r_e_l_a_t_i_o_n _S_t_r_u_c_t_u_r_e

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

     This function is a constructor for the `corCAR1' class,
     representing an autocorrelation structure of order 1, with a
     continuous time covariate. Objects created using this constructor
     must be later initialized using the appropriate `initialize'
     method.

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

     corCAR1(value, form, fixed)

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

   value: the correlation between two observations one unit of time
          apart. Must be between 0 and 1. Defaults to 0.2.

    form: a one sided formula of the form `~ t', or `~ t | g',
          specifying a time covariate `t' and,  optionally, a grouping
          factor `g'. Covariates for this correlation structure need
          not be integer valued.  When a grouping factor is present in
          `form', the correlation structure is assumed to apply only to
          observations within the same grouping level; observations
          with different grouping levels are assumed to be
          uncorrelated. Defaults to `~ 1', which corresponds to using
          the order of the observations in the data as a covariate, and
          no groups.

   fixed: an optional logical value indicating whether the coefficients
          should be allowed to vary in the optimization, or kept fixed
          at their initial value. Defaults to `FALSE', in which case
          the coefficients are allowed to vary.

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

     an object of class `corCAR1', representing an autocorrelation
     structure of order 1, with a continuous time covariate.

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

     Jose Pinheiro and Douglas Bates

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

     Box, G.E.P., Jenkins, G.M., and Reinsel G.C. (1994) "Time Series
     Analysis: Forecasting and Control", 3rd Edition, Holden-Day.

     Jones, R.H. (1993) "Longitudinal Data with Serial Correlation: A
     State-space Approach", Chapman and Hall

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

     `initialize.corStruct'

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

     ## covariate is Time and grouping factor is Mare
     cs1 <- corCAR1(0.2, form = ~ Time | Mare)

