function [ACOVF,ACF,PACF,ARPmx,PE,ACFsd]=invest0(Y,Pmax);
% First Investigation of a signal (time series)
% [ACOVF,ACF,PACF,ARPmx,E]=invest0(Y,Pmax);
%
% Y	time series
% Pmax	maximal order (optional)
%
% ACOVF	Autocovariance 
% ACF	Autocorrelation
% PACF	Partial Autocorrelation
% ARPmx Autoregressive Parameter Matrix
% 	ARPmx(p,1:p) gives the AR-parameters for model order p
% E	Error function E(p)

%	Version 2.44%	last revision 22.06.1998
%	Copyright (c) 1996-1998 by Alois Schloegl
%	e-mail: a.schloegl@ieee.org	

% This library is free software; you can redistribute it and/or
% modify it under the terms of the GNU Library General Public
% License as published by the Free Software Foundation; either
% Version 2 of the License, or (at your option) any later version.
%
% This library is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
% Library General Public License for more details.
%
% You should have received a copy of the GNU Library General Public
% License along with this library; if not, write to the
% Free Software Foundation, Inc., 59 Temple Place - Suite 330,
% Boston, MA  02111-1307, USA.

N=length(Y); 
if nargin<2 Pmax=min([100 N/3]); end;
M=min(Pmax+1,N-1);
ACOVF=acovf(Y,M);
ACF=ACOVF(2:M+1)/ACOVF(1);
ACFsd=ones(1,M)*sqrt(1/N);
[PACF,ARPmx,PE]=acf2pacf(ACF,ACOVF(1));
%E=ACOVF(1)-[0 ACOVF(2:M+1)*ARPmx'];
%[PACF,ARPmx,E]=acf2pacf(ACF);