CCA                package:clim.pact                R Documentation

_C_a_n_o_n_i_c_a_l _c_o_r_r_e_l_a_t_i_o_n _a_n_a_l_y_s_i_s

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

     Applies a canonical correlation analysis (CCA) to two data sets.
     The CCA here can be carried out in two different ways: i) an 'svd'
     based approach (after  Bretherton et al. (1992), J. Clim. Vol 5,
     p. 541, also documented in Benestad (1998):  "Evaluation of
     Seasonal Forecast Potential for Norwegian  Land Temperatures and
     Precipitation using CCA",  DNMI KLIMA Report 23/98 at <URL:
     http://met.no/english/r_and_d_activities/publications/1998.html>)
     or ii) a  covariance-eigenvalue approach (after Wilks, 1995,
     "Statistical methods in the Atmospheric  Sciences", Academic
     Press, p. 401). 

     The analysis can also be applied to either EOFs or fields.

     Note: the analysis has sometimes been somewhat unstable, returning
     inconsistent results. The recommendation is to use EOFs and SVD
     option.

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

     CCA(x1,x2,SVD=TRUE,plot=TRUE,main="CCA",sub="",test=FALSE,i.eofs=1:8,LINPACK=TRUE)
     testCCA(method="CCA",reconstr=FALSE,mode=1,test=TRUE,LINPACK=TRUE,SVD=TRUE,n.pc=4,synthetic=TRUE)

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

      x1: A field or an eof object.

      x2: A field or an eof object.

     SVD: Flag: determine which approach to use: SVD or
          eigenfunction-based alogithm.

    plot: Flag: plot the diagnostics.

    test: Flag: test by reconstructing one series (leading EOF or a
          grid-box series).

  i.eofs: Which EOFs to include (only when the input is given as eof
          objects).

 LINPACK: 'TRUE': svd; 'FALSE':La.svd

    main: main title (see 'link{plot}').

     sub: subtitle (see 'link{plot}').

  method: Which method to test: CCA or MVR.

reconstr: For the test-reconstruction of fields.

    mode: Test for a particular EOF pattern/mode - the other modes are
          randomized.

    n.pc: Number of principal components to include.

synthetic: Construct artificial test data from a random number
          generator and cosine series.

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

     A CCA object: a list containing a.m, b.m, u.k, v.k, and r,
     describing the Canonical Correlation variates, patterns and
     correlations. a.m and b.m are the patterns and u.k and v.k the
     vectors (time evolution).)

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

     R.E. Benestad

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

     ## Not run: 
     data(DNMI.t2m)
     data(DNMI.slp)
     eof.1 <- EOF(DNMI.t2m,mon=1)
     eof.2 <- EOF(DNMI.slp,mon=1)
     cca <- CCA(eof.1,eof.2)
     # Testing routine:
     testCCA()
     ## End(Not run)

