error message using polyfit (nonlinear regression)
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hi,
I get the following error meesage using the polyfit function:
Warning: Polynomial is badly conditioned. Add points with distinct X
values, reduce the degree of the polynomial, or try centering
and scaling as described in HELP POLYFIT.
Has anybody see that before and has an idea what I need to do? I tried it with the help function, but I didn't understand what excatly could be false
the code I am using if the following, in case it helps:
if length(dataT(:,1))==1
SlopeSkew(number)=0;
elseif length(dataT(:,1))==2
SlopeSkew(number)=0;
else
% x is the Strike
x= dataT(:,2);
%is the implied volatility
y=dataT(:,10);
p = polyfit(x,y,2);
f = polyval(p,x);
thanks!
a=p(3);
b=p(2);
c=p(1);
SlopeSkew(number)=b+2*c.*x;
Slope=SlopeSkew';
采纳的回答
Image Analyst
2013-4-21
0 个投票
So what is the length of x and y, and do you have any repeated x values?
4 个评论
Locks
2013-4-21
Image Analyst
2013-4-21
编辑：Image Analyst
2013-4-21
That did not answer the questions.
Locks
2013-4-21
Image Analyst
2013-4-21
If you have two Y values for the same x value, then it doesn't like that and will complain. All your x values have to be unique. You can use
uniqueX = unique(x)
and see if length(uniqueX) is the same length as length(x). If they're the same then there are no repeats. If unique() returns a shorter vector, then at least one x value is repeated and you need to decide how to handle that. You might be able to just add a very small amount to one of the x's, like 0.000001, just to make sure they are not the same anymore.
更多回答（2 个）
Tom Lane
2013-4-22
There was a time when this function issued an error asking you not to have repeated X values. But the new error message is more accurate. You don't need unique X values. It's just that repeated X values won't allow you to estimate higher-order polynomials. So for instance:
x = [1;2;3;3;4];
y = (1:5)';
polyfit(x,y,2)
polyfit(x,y,4)
The first call to polyfit works. The second would work if we had 5 points with distinct X values, but it doesn't work here because the 4 distinct X values allow polynomials only up to an exponent of 3.
In your example of fitting up to power 2, it seems like you either don't have 3 distinct points, or you have very ill-conditioned data.
2 个评论
Locks
2013-4-22
Tom Lane
2013-4-23
I don't know exactly. Of the following three, the first one works. The second does not because there are only two distinct x values. The third does not because it is very ill-conditioned.
polyfit([0;1;2],[10;20;30],2);
polyfit([0;1;1],[10;20;30],2);
polyfit([0;eps;1],[10;20;30],2);
I don't know what the issue is in your case. Try to boil this down to a specific call to polyfit, then examine the x and y values in that call and see how they look.
Jan
2013-4-23
Instead of repeated values, did you test the condition of the problem already? The docs suggest to use
[p, S, mu] = polyfit(x,y,n)
for a proper scaling. The matrix for the least-squares fit is ill-conditioned, when the values of x have a wide range and are far away from zero. Therefore the scaling does:
xx = (x - mean(x)) / std(x)
to get all data near to zero. The conversion back to the original values in POLIVAL is trivial.
3 个评论
Image Analyst
2013-4-23
I use s and mu when it specifically tells me to, not by default, though it certainly can't hurt, though, like you said, there's the extra step of converting the output of polyval. He says, and MATLAB also says, that there are repeated values so I think that will have to be fixed.
Locks
2013-4-23
Locks
2013-4-24
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