lfo.ar                package:RTisean                R Documentation

_M_o_d_e_l_i_n_g _d_a_t_a _t_h_r_o_u_g_h _a _l_o_c_a_l _l_i_n_e_a_r _a_n_s_a_t_z

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

     Makes a local linear ansatz and estimates the one step  prediction
     error of the model.

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

     lfo.ar(series, l, x = 0, c = 1, m = c(1,2), d = 1, i, r, R, f = 1.2, s = 1, C)

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

  series: a vector or a matrix. 

       l: number of points to use.  

       x: number of lines to be ignored. 

       c: column to be read.  

       m: no. of components, embedding dimension

       d: delay for the embedding. 

       i: number of points for which the error should be calculated. 

       r: neighborhood size to start with. 

       R: neighborhood size to end with. 

       f: factor to increase the neighborhood size if not enough
          neighbors were found.

       s: steps to be forecasted.

       C: width of causality window.

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

     A matrix containing: the neighborhood size in column 1; the
     relative forecast error in columnn 2; the fraction of points for
     which neighbors were found for the corresponding neighborhood size
     in column 3; the average number of neighbors found per point in
     column 4; the variance of the fraction of points for which
     neighbors were found in column 5.

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

     M. Casdagli, Chaos and deterministic versus stochastic nonlinear
     modeling, J. Roy.  Stat. Soc. 54, 303 (1991).

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

     'onestep'

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

     ## Not run: 

     dat <- logistic(iter=1000,r=3.6) +runif(1000)/10
     ll_aroutput <- lfo.ar(dat)
     par(mfrow=c(2,2))

     plot(ll_aroutput[,1],ll_aroutput[,2],xlab="Neighborhood size",ylab="Relative forecast error",t="l")
     plot(ll_aroutput[,1],ll_aroutput[,3],xlab="Neighborhood size", ylab="Fraction of points with neighbors",t="l" )
     plot(ll_aroutput[,1],ll_aroutput[,4],xlab="Neighborhood size",ylab="Average number of neighbors",t="l")
     plot(ll_aroutput[,1],ll_aroutput[,5],xlab="Neighborhood size",ylab="Variance of points with neighbors",t="l")

     ## End(Not run)

