project               package:RTisean               R Documentation

_P_r_o_j_e_c_t_i_v_e _n_o_n_l_i_n_e_a_r _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 locally projective nonlinear noise reduction of a time
     series.

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

     project(series, m, q, r, k, i = 1, l, x = 0, c = 1)

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

  series: a vector or a matrix. 

       m: embedding dimension.  

       q: dimension of manifold.

       r: radius of neighbourhoods. 

       k: minimal number of neighbours. 

       i: number of iterations. 

       l: number of values to be read. 

       x: number of values to be skipped. 

       c: column to be read. 

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

     A matrix containing the filtered time series in the first column
     and the difference between the original and the filtered time
     series in the second column.

_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);  Reprinted in: E. Ott, T. Sauer, and J. A. Yorke,
     eds.,  Coping With Chaos, Wiley, New York (1994)

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

     'ghkss'

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

     ## Not run: 

     x <- 1:500
     y <- cos(x/100)^2 - cos(x/200)+ rnorm(500)/10
     filteredy <- project(y,m=7,q=2,k=10,r=1)
     plot(x, y, t="l", xlab="Time", ylab="Time series", main="Projective nonlinear noise reduction")
     lines(x, filteredy[,1], col=2,lwd=1.5)
     legend(350,0, c("Noisy data","Filtered data"),fill=c(1,2), bty="n")

     ## End(Not run)

