ctsdiag                 package:cts                 R Documentation

_D_i_a_g_n_o_s_t_i_c _P_l_o_t_s _f_o_r _T_i_m_e-_S_e_r_i_e_s _C_A_R _F_i_t_s

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

     A generic function to plot time-series diagnostics.

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

     ctsdiag(object, gof.lag = 10, ...)

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

  object: a fitted time-series CAR model

 gof.lag: the maximum number of lags for a Portmanteau goodness-of-fit
          test

     ...: further arguments to be passed to particular methods

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

     This is a generic function. It will generally plot the residuals,
     often standadized, the autocorrelation function of the residuals,
     and the p-values of a Portmanteau test for all lags up to
     'gof.lag'.

     The method for 'car' object plots residuals scaled by the estimate
     of their (individual) variance, and use the Ljung-Box version of
     the portmanteau test.

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

     None. Diagnostics are plotted.

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

     G. Tunnicliffe Wilson and Zhu Wang

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

     Belcher, J. and Hampton, J. S. and Tunnicliffe Wilson, G. (1994).
     Parameterization of continuous time autoregressive models for
     irregularly sampled time series data. _Journal of the Royal
     Statistical Society, Series B, Methodological_,*56*,141-155

     Jones, Richard H. (1981). Fitting a continuous time autoregression
     to discrete data. _Applied Time Series Analysis II_, 651-682

     Wang, Zhu(2004). _The Application of the Kalman Filter to
     Nonstationary Time Series through Time Deformation_. PhD thesis,
     Southern Methodist University

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

     'car'

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

     ## Not run: 
     data(V22174)
     (fit <- car(V22174,scale=0.2,order=7))
     ctsdiag(fit)
     ## End(Not run)

