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function [model_est, var_est, msg_strg] = arParEst(data_in, order, method)
checkNArgin(3, 3, argn(2))
model_est = [];
var_est = [];
if isvector(data_in) then
data_in = data_in(:);
end
select method
case 'covariance' then
x_min_len = 2*order;
case 'modified' then
x_min_len = (3*order)/2;
else
msg_strg = 'Error in function arParEst: Unknown estimation method'
return;
end
if size(data_in, 1)< x_min_len then
if (strcmp(method, 'covariance') == 0) then
if isvector(data_in) then
msg_strg = 'length of input vector should be greater than 2 times of the input order of the model'
return
else
msg_strg = 'number of rows in input matrix should be greater than 2 times of the input order of the model'
return
end
elseif (strcmp(method, 'modified') == 0) then
if isvector(data_in) then
msg_strg = 'length of input vector should be greater than 3/2 times of the input order of the model'
return
else
msg_strg = 'number of rows in input matrix should be greater than 3/2 times of the input order of the model'
return
end
end
end
if issparse(data_in) then
msg_strg = 'Input data should not be sparse';
return
end
if isempty(order) | order ~= round(order) then
msg_strg = 'Model order should be an integer';
return
end
msg_strg = [];
model_est = zeros((order + 1), size(data_in, 2));
var_est = zeros(1, size(data_in, 2));
for i = 1: size(data_in, 2)
data_corrmtx = corrmtx(data_in(:, i), order, method);
data_allcols = data_corrmtx(:, 2:size(data_corrmtx,2));
data_sincol = data_corrmtx(:, 1);
model_est(:, i) = [1; -data_allcols\data_sincol];
var_tmp = data_sincol'*data_allcols;
var_est(:, i) = data_sincol'*data_sincol + var_tmp*model_est(2:size(model_est, 1), i);
var_est(:, i) = abs(real(var_est(:, i)));
end
model_est = model_est.';
endfunction
function checkNArgin(min_argin, max_argin, num_of_argin)
if num_of_argin < min_argin then
error('Not enough input arguments')
end
if num_of_argin > max_argin then
error('Too many input arguments')
end
endfunction
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