decevf                package:pastecs                R Documentation

_T_i_m_e _s_e_r_i_e_s _d_e_c_o_m_p_o_s_i_t_i_o_n _u_s_i_n_g _e_i_g_e_n_v_e_c_t_o_r _f_i_l_t_e_r_i_n_g (_E_V_F)

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

     The eigenvector filtering decomposes the signal by applying a
     principal component analysis (PCA) on the original signal and a
     certain number of copies of it incrementally lagged, collected in
     a multivariate matrix. Reconstructing the signal using only the
     most representative eigenvectors allows filtering it.

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

     decevf(x, type="additive", lag=5, axes=1:2)

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

       x: a regular time series ('rts' under S+ and 'ts' under R) 

    type: the type of model, either 'type="additive"' (by default), or
          'type="multiplicative"' 

     lag: The maximum lag used. A PCA is run on the matrix constituted
          by vectors lagged from 0 to 'lag'. The defaulf value is 5,
          but a value corresponding to no significant autocorrelation,
          on basis of examination of the autocorrelation plot obtained
          by 'acf' in the library 'ts' should be used (Lag at first
          time the autocorrelation curve crosses significance lines
          multiplied by the frequency of the series). 

    axes: The principal axes to use to reconstruct the filtered signal.
          For instance, to use axes 2 and 4, use 'axes=c(2,4)'. By
          default, the first two axes are considered ('axes=1:2') 

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

     a 'tsd' object

_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:

     Colebrook, J.M., 1978. _Continuous plankton records: zooplankton
     and environment, North-East Atlantic and North Sea 1948-1975._
     Oceanologica Acta, 1:9-23.

     Ibanez, F. & J.C. Dauvin, 1988. _Long-term changes (1977-1987) on
     a muddy fine sand Abra alba - Melinna palmate population community
     from the Western English Channel._ J. Mar. Prog. Ser., 49:65-81.

     Ibanez, F., 1991. _Treatment of 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._ Pp. 5-43.

     Ibanez, F. & M. Etienne, 1992. _Le filtrage des sries
     chronologiques par l'analyse en composantes principales de
     processus (ACPP)._ J. Rech. Ocanogr., 16:27-33.

     Ibanez, F., J.C. Dauvin & M. Etienne, 1993. _Comparaison des
     volutions  long-terme (1977-1990) de deux peuplements
     macrobenthiques de la Baie de Morlaix (Manche Occidentale):
     relations avec les facteurs hydroclimatiques._ J. Exp. Mar. Biol.
     Ecol., 169:181-214.

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

     'tsd', 'tseries', 'decaverage', 'deccensus', 'decmedian', 
     'decdiff', 'decreg', 'decloess'

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

     data(releve)
     melo.regy <- regul(releve$Day, releve$Melosul, xmin=9, n=87,
             units="daystoyears", frequency=24, tol=2.2, methods="linear",
             datemin="21/03/1989", dateformat="d/m/Y")
     melo.ts <- tseries(melo.regy)
     acf(melo.ts)
     # Autocorrelation is not significant after 0.16
     # That corresponds to a lag of 0.16*24=4 (frequency=24)
     melo.evf <- decevf(melo.ts, lag=4, axes=1)
     plot(melo.evf, col=c(1, 4, 2))
     # A superposed graph is better in the present case
     plot(melo.evf, col=c(1, 4), xlab="stations", stack=FALSE, resid=FALSE,
             lpos=c(0, 60000))

