AutoD2                package:pastecs                R Documentation

_A_u_t_o_D_2, _C_r_o_s_s_D_2 _o_r _C_e_n_t_e_r_D_2 _a_n_a_l_y_s_i_s _o_f _a _m_u_l_t_i_p_l_e _t_i_m_e-_s_e_r_i_e_s

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

     Compute and plot multiple autocorrelation using Mahalanobis
     generalized distance D2. AutoD2 uses the same multiple
     time-series. CrossD2 compares two sets of multiple time-series
     having same size (same number of descriptors). CenterD2 compares
     subsamples issued from a single multivariate time-series, aiming
     to detect discontinuities.

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

     AutoD2(series, lags=c(1, nrow(series)/3), step=1, plotit=TRUE,
             add=FALSE, ...)
     CrossD2(series, series2, lags=c(1, nrow(series)/3), step=1,
             plotit=TRUE, add=FALSE, ...)
     CenterD2(series, window=nrow(series)/5, plotit=TRUE, add=FALSE,
             type="l", level=0.05, lhorz=TRUE, lcol=2, llty=2, ...)

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

  series: regularized multiple time-series 

 series2: a second set of regularized multiple time-series 

    lags: minimal and maximal lag to use. By default, 1 and a third of
          the number of observations in the series respectively 

    step: step between successive lags. By default, 1 

  window: the window to use for CenterD2. By default, a fifth of the
          total number of observations in the series 

  plotit: if `TRUE' then also plot the graph 

     add: if `TRUE' then the graph is added to the current figure 

    type: The type of line to draw in the CenterD2 graph. By default, a
          line without points 

   level: The significance level to consider in the CenterD2 analysis.
          By default 5% 

   lhorz: Do we have to plot also the horizontal line representing the
          significance level on the graph? 

    lcol: The color of the significance level line. By default, color 2
          is used 

    llty: The style for the significance level line. By default:
          `llty=2', a dashed line is drawn

     ...: additional graph parameters 

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

     An object of class 'D2' which contains: 

    lag : The vector of lags

     D2 : The D2 value for this lag

   call : The command invoked when this function was called

   data : The series used

   type : The type of 'D2' analysis: 'AutoD2', 'CrossD2' or 'CenterD2'

 window : The size of the window used in the CenterD2 analysis

  level : The significance level for CenterD2

  chisq : The chi-square value corresponding to the significance level
          in the CenterD2 analysis

units.text : Time units of the series, nicely formatted for graphs

_W_A_R_N_I_N_G:

     If data are too heterogeneous, results could be biased (a
     singularity matrix appears in the calculations).

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

     Frdric Ibanez (ibanez@obs-vlfr.fr), Philippe Grosjean
     (phgrosjean@sciviews.org)

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

     Cooley, W.W. & P.R. Lohnes, 1962. Multivariate procedures for the
     behavioural sciences. Whiley & sons.

     Dagnlie, P., 1975. Analyse statistique  plusieurs variables.
     Presses Agronomiques de Gembloux.

     Ibanez, F., 1975. Contribution  l'analyse mathmatique des
     vnements en cologie planctonique: optimisations
     mthodologiques; tude exprimentale en continu  petite chelle
     du plancton ctier. Thse d'tat, Paris VI.

     Ibanez, F., 1976. Contribution  l'analyse mathmatique des
     vnements en cologie planctonique. Optimisations
     mthodologiques. Bull. Inst. Ocanogr. Monaco, 72:1-96.

     Ibanez, F., 1981. Immediate detection of heterogeneities in
     continuous multivariate oceanographic recordings. Application to
     time series analysis of changes in the bay of Villefranche sur
     mer. Limnol. Oceanogr., 26:336-349.

     Ibanez, F., 1991. Treatment of the data deriving from the COST 647
     project on coastal benthic ecology: The within-site analysis. In:
     B. Keegan (ed), Space and time series data analysis in coastal
     benthic ecology, p 5-43.

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

     `mahalanobis', `acf'

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

     data(marphy)
     marphy.ts <- as.ts(as.matrix(marphy[, 1:3]))
     AutoD2(marphy.ts)
     marphy.ts2 <- as.ts(as.matrix(marphy[, c(1, 4, 3)]))
     CrossD2(marphy.ts, marphy.ts2)
     # This is not identical to:
     CrossD2(marphy.ts2, marphy.ts)
     marphy.d2 <- CenterD2(marphy.ts, window=16)
     lines(c(17, 17), c(-1, 15), col=4, lty=2)
     lines(c(25, 25), c(-1, 15), col=4, lty=2)
     lines(c(30, 30), c(-1, 15), col=4, lty=2)
     lines(c(41, 41), c(-1, 15), col=4, lty=2)
     lines(c(46, 46), c(-1, 15), col=4, lty=2)
     text(c(8.5, 21, 27.5, 35, 43.5, 57), 11, labels=c("Peripheral Zone", "D1",
             "C", "Front", "D2", "Central Zone")) # Labels
     time(marphy.ts)[marphy.d2$D2 > marphy.d2$chisq]

