checkResiduals             package:dse1             R Documentation

_A_u_t_o_c_o_r_r_e_l_a_t_i_o_n_s _D_i_a_g_n_o_s_t_i_c_s

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

     Calculate autocorrelation diagnostics of a time series  matrix or
     TSdata or residuals of a TSestModel

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

         checkResiduals(obj, ...)
         ## Default S3 method:
         checkResiduals(obj, ac=TRUE, pac=TRUE, select=seq(nseries(obj)), 
                        drop=NULL, plot.=TRUE, graphs.per.page=5, verbose=FALSE, ...)
         ## S3 method for class 'TSdata':
         checkResiduals(obj, ...)
         ## S3 method for class 'TSestModel':
         checkResiduals(obj, ...)

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

     obj: An TSestModel or TSdata object.

      ac: If TRUE the auto-correlation function is plotted.

     pac: If TRUE the partial auto-correlation function is plotted.

  select: Is used to indicate a subset of the residual series. By
          default all residuals are used.

    drop: Is used to indicate a subset of the residual time periods to
          drop.  All residuals are used with the default
          (NULL).Typically this can be used  to get rid of bad initial
          conditions (eg. drop=seq(10) ) or outliers.

   plot.: If FALSE then plots are not produced.

graphs.per.page: Integer indicating number of graphs to place on a
          page.

 verbose: If TRUE then the auto-correlations and partial
          auto-correlations  are printed if they are calculated.

     ...: arguments passed to other methods.

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

     This is a generic function. The default method works for a time
     series matrix which is treated as if it were a matrix of
     residuals. However, in a Box-Jenkins type of analysis the matrix
     may be data which is being evaluated to determine a model. The
     method for a TSestModel evaluates the residuals calculated by
     subtracting the output data from the model predictions.

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

     A list with residual diagnostic information: residuals, mean, cov,
      acf= autocorrelations, pacf= partial autocorrelations.

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

         if(is.R()) data("eg1.DSE.data.diff", package="dse1")
         model <- estVARXls(eg1.DSE.data.diff)
         checkResiduals(model)

