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least square method.getting error using '\' or linsolve. pls help

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any kind folks who can help me out. i'm trying to implement least square method, but i'm getting some error using linsolve and '\' function. please help, and million thanks in advance!
for a =1:400
for P=1:49
o=(P-1)*243;
for r=1:35
if r==35
t=-2;
pA1(1:5,1,r,P)=A(7*r-6+o:7*r+t+o,a);
pB1(1:5,1,r,P)=B(7*r-6+o:7*r+t+o,a);
beta(r,P)=linsolve(pA1,pB1); <--- error
% beta(r,P)=pA1\pB1;
else
t=0;
pA(1:7+t,1,r,P)=A(7*r-6+o:7*r+t+o,a);
pB(1:7+t,1,r,P)=B(7*r-6+o:7*r+t+o,a);
beta(r,P)=linsolve(pA,pB);; <---- error
% beta(r,P)=pA\pB;
end
end
end
  2 Commenti
Stephen23
Stephen23 il 23 Gen 2015
Can you please tell us what the complete error message is. We cannot read minds, and we also cannot read what is on your computer screen.
seprienna
seprienna il 23 Gen 2015
many thanks for willing to help me out.
The complete message for linsolve is as follows:
Error using linsolve
First and second arguments must be 2D.
Error in modifiedregression (line 34)
beta(r,P)=linsolve(pA,pB);
whereas for \ is as follows:
Error using \
Inputs must be 2-D, or at least one input must be scalar.
To compute elementwise LDIVIDE, use LDIVIDE (.\) instead.
Error in modifiedregression (line 36)
beta(r,P)=pA\pB;

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Risposte (1)

Matt J
Matt J il 23 Gen 2015
Modificato: Matt J il 23 Gen 2015
Your pA1 and pB1 are 4D arrays. However linsolve(A,B) expects A and B to be 2D matrices, and similarly for '\'.
  4 Commenti
seprienna
seprienna il 23 Gen 2015
i'm still trying to figure out a method to overcome the arrays to matrices. any tips from you? your kind reply is greatly appreciated
Matt J
Matt J il 23 Gen 2015
Nope, no tips. We have no way of understanding what your code is trying to do unless you explain it to us.

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