plot methods              package:tsDyn              R Documentation

_P_l_o_t_t_i_n_g _m_e_t_h_o_d_s _f_o_r _s_e_t_a_r _a_n_d _l_s_t_a_r _s_u_b_c_l_a_s_s_e_s

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

     Plotting methods 'setar' and 'lstar' subclasses

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

     ## S3 method for class 'setar':
     plot(x, ask=interactive(), legend=FALSE, regSwStart, regSwStop, ...)
     ## S3 method for class 'lstar':
     plot(x, ask=interactive(), legend=FALSE, regSwStart, regSwStop, ...)

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

       x: fitted 'setar' or 'lstar' object 

     ask: graphical option. See 'par'

  legend: Should a legend be plotted? (logical)

regSwStart, regSwStop: optional starting and stopping time indices for
          regime switching plot 

     ...: further arguments to be passed to and from other methods 

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

     These plot methods produce a plot which gives to you an idea of
     the behaviour of the fitted model.

     Firstly, if embedding dimension is, say, m, m scatterplots are
     produced. On the x axis you have the lagged time series values. On
     the y axis the 'response' time series values. Observed points are
     represented with different colors-symbols depending on the level
     of the threshold variable. Specifically, for the setar model,
     black means 'low regime', red means 'high regime'. For the lstar
     model, where the self-threshold variable is continuous, threshold
     values are grouped in 5 different zones with the same number of
     points in each. Note that if more than 300 points are to be
     plotted, they all share the same symbol, and regimes can be
     distinguished only by color. If you want, by specifying
     'legend=TRUE' a legend is added at the upper-left corner of each
     scatterplot. To each scatterplot, a dashed line is superposed,
     which links subsequent fitted values.

     Finally, a new time series plot is produced, with lines segments
     coloured depending on the regime (colors meanings are the same of
     those in the preceedings scatterplots). Optionally, you can
     specify a starting and ending time indices, for zooming on a
     particular segment of the time series.

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

     Antonio, Fabio Di Narzo

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

     'setar', 'lstar'

     nlar-methods for other generic available methods for this kind of
     objects.

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

     ##
     ##See 'setar' examples
     ##

