Considering the following nice solution for finding cubic Bézier control points for a curve passing through 4 points:
If I have some (x,y) data, I can easily draw straight-line through it, e.g. f=glm(y~x) plot(x,y) lines(x,f$fitted.values)
I have a probability density function in a plot called ph that i derived from two samples of data, by the help of a user of stackoverflow, in this way
I try to calculate a line that can fit given several points with 2-d coordinate in MATLAB. But theresult is not I expected. There may be somethin开发者_Python百科g I understand wrong. Can anyone help
I am 开发者_如何学JAVAcurrently using numpy.polyfit(x,y,deg) to fit a polynomial to experimental data. I would however like to fit a polynomial that uses weighting based on the errors of the points.
I\'m trying 开发者_JAVA技巧from 2 days to curve fit some data that I have using this equation f = @(x) ((x.^4) .* exp(x)) ./((exp(x)-1).^2);
I have a set of data points (which I can thin out) that I need to fit with a Bézier curve.I need speed over accuracy, but the fit should be decent enough to 开发者_运维百科be recognizable. I\'m also
I want to carry out a linear regression in R for data in a normal and in a double logarithmic plot. For normal data the dataset might be the follwing:
Is it possible to draw an abline of a fit only in a certain range of x-values? I have a dataset with a linear fit of a subset of that dataset:
I am trying to use GSL for least square fitting, but I can\'t even make the example run properly. It compiles, but when running the program it gives the error
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