ghkss                package:RTisean                R Documentation

_N_o_i_s_e _r_e_d_u_c_t_i_o_n

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

     Performs a noise reduction, through an orthogonal projection onto
     manifold using an  euclidean or a special metric.

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

     ghkss(series, l, x = 0, c = 1, m = 5, d = 1, q = 3, k = 30, r, i = 1, two = FALSE)

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

  series: a vector or a matrix. 

       l: number of data to use. 

       x: number of lines to be ignored.

       c: column to be read. 

       m: embedding dimension. 

       d: delay for the embedding. 

       q: dimension of the manifold to project to. 

       k: minimal number of neighbours. 

       r: minimal size of the neighbourhood. 

       i: number of iterations.

     two: use euclidean metric instead of the special one.

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

     A list of vectors with filtered time series, one per iteration.

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

     P. Grassberger, R. Hegger, H. Kantz, C. Schaffrath, and T.
     Schreiber, On noise reduction methods for chaotic data, CHAOS 3,
     127 (1993).

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

     'project'

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

     ## Not run: 

     #FIXME: find better example
     dat <- logistic(iter=10000,r=3.8, noisesd=0.01)
     filteredat <- ghkss(dat)[[1]]
     delayedfiltered <- embed(filteredat, d=2)
     delayed <- embed(dat,d=2)
     plot(delayed,cex=0.8,xlab="x(t)",ylab="x(t+1)",main="Delayed Logistic Chaotic Attractor",pch='.')
     points(delayedfiltered,col=2,cex=0.8,pch='.')
     legend(0.5,0.4, c("Noisy data","Filtered data"),fill=c(1,2), bty="n",cex=0.8)

     ## End(Not run)

