Surrogate test
Contents
Plotting linear and nolinear process with gaussian fit
Nonlinear process: Hennon map
Xnl = generate_henon (1000); h(1)=axes('Position',[.06 .08 .4 .4]); plot(Xnl,':.'),axis([0 1000 -2 2]) h(2)=axes('Position',[.06 .58 .4 .4]); histfit(Xnl) % Linear Process: linear ARMA AR(3) Xlin = generate_ar_signal ([.5 -.3 .3], 1000); h(3)=axes('Position',[.56 .08 .4 .4]); plot(Xlin,':.'),axis([0 1000 -5 5]) h(4)=axes('Position',[0.56 0.58 .4 .4]); histfit(Xlin) set(h,'Visible','on');
![](report_01.png)
testing the Null hypothesis
The line does not lie within the null distribution
Xnl = generate_henon (1000); Tnl = comp_rev (Xnl); n_surr = 24; % Number of surrogates Tnls = zeros(n_surr,1); for n=1:n_surr, Xs = generate_iAAFT(Xnl);Tnls(n) = comp_rev(Xs);end figure,hist(Tnls),A=axis;line([Tnl Tnl], A(3:4));
![](report_02.png)
The line does lie within the null distribution
Xlin = generate_ar_signal ([.5 -.3 .3], 1000); Tlin = comp_rev (Xlin); n_surr = 24; % Number of surrogates Tlins = zeros(n_surr,1); for n=1:n_surr, Xs = generate_iAAFT(Xlin);Tlins(n) = comp_rev(Xs);end figure,hist(Tlins),A=axis;line([Tlin Tlin], A(3:4));
![](report_03.png)