Want to fit linear curve on my data.
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I have x, y data
x=[0;0.100000000000000;0.200000000000000;0.300000000000000;0.400000000000000;0.500000000000000;0.600000000000000];
y=[4.67178152947921e-06;4.67353333624452e-06;4.70560728038426e-06;4.74873086195845e-06;4.77333265701103e-06;4.84630647442201e-06;4.87015810633671e-06];
I want to plot x vs y and want to y-axis in log scale
plot(x,y)
set(gca,'YScale','log')
hold on
Note: x data starts from 0
Now I want to fit a line and show the slope of that curve fitting line + original curve
p=polyfit(x,(y),1);
q=polyval(p,x);
plot(x,q).
It seems to be not right because the fit line isn't straight ( it likes power fit or exponential) . Note log scale ( not log(data))
Please helps. Thanks
2 Kommentare
the cyclist
am 18 Mär. 2013
It would be tremendously helpful if you included a small sample of your data that exhibits the problem, so that we could run your code and see the results. Your statement that it "seems to be not right" is not quite enough.
Akzeptierte Antwort
Daniel Shub
am 18 Mär. 2013
Bearbeitet: Daniel Shub
am 20 Mär. 2013
I don't like working on log scales, I would rather take the log transform of my data
x=[0;0.100000000000000;0.200000000000000;0.300000000000000;0.400000000000000;0.500000000000000;0.600000000000000];
y=[4.67178152947921e-06;4.67353333624452e-06;4.70560728038426e-06;4.74873086195845e-06;4.77333265701103e-06;4.84630647442201e-06;4.87015810633671e-06];
xx = x;
yy = log(y);
plot(xx, yy, '*');
lsline;
p = polyfit(xx, yy,1);
text(max(xlim), max(ylim), ['Slope: ', num2str(p(1))], 'HorizontalAlignment', 'Right', 'VerticalAlignment', 'Top')
set(gca, 'YTickLabel', 10^6*exp(get(gca, 'YTick')))
4 Kommentare
Daniel Shub
am 20 Mär. 2013
I got the output of polyfit wrong (I thought the first coefficient was the intercept). I changed the code. I also changed the scaling of the ticks. You can set them to be whatever you want.
Weitere Antworten (1)
Jan
am 19 Mär. 2013
When you want a line in the logspace diagram, you need an exponential fit on the data. Or build the log of the data at first and fit the line afterwards.
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