Fitting a straight line to a log-log curve in matplotlib

Question

I have a plot with me which is logarithmic on both the axes. I have pyplot's loglog function to do this. It also gives me the logarithmic scale on both the axes.

Now, using numpy I fit a straight line to the set of points that I have. However, when I plot this line on the plot, I cannot get a straight line. I get a curved line.

The blue line is the supposedly "straight line". It is not getting plotted straight. I want to fit this straight line to the curve plotted by red dots

Here is the code I am using to plot the points:

import numpy
from matplotlib import pyplot as plt
import math
fp=open("word-rank.txt","r")
a=[]
b=[]

for line in fp:
    string=line.strip().split()
    a.append(float(string[0]))
    b.append(float(string[1]))

coefficients=numpy.polyfit(b,a,1)
polynomial=numpy.poly1d(coefficients)
ys=polynomial(b)
print polynomial
plt.loglog(b,a,'ro')
plt.plot(b,ys)
plt.xlabel("Log (Rank of frequency)")
plt.ylabel("Log (Frequency)")
plt.title("Frequency vs frequency rank for words")
plt.show()

Answer 1

To better understand this problem, let's first talk about plain ol' linear regression (the polyfit function, in this case, is your linear regression algorithm).

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Suppose you have a set of data points (x,y), shown below:

You want to create a model that predicts y as a function of x, so you use linear regression. That uses the model:

y = mx + b

and computes the values of m and b that best predict your data, using some linear algebra.

Next, you use your model to predict values of y as a function of x. You do this by picking a set of values for x (think linspace) and computing the corresponding values of y. Plotting these (x,y) pairs gives you your regression line.

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Now, let's talk about logarithmic regression. In this case, we still have two variables, y versus x, and we are still interested in their relationship, i.e., being able to predict y given x. The only difference is, now y and x happen to be logarithms of two other variables, which I'll call log(F) and log(R). Thus far, this is nothing more than a simple change of name.

The linear regression also works the same way. You're still regressing y versus x. The linear regression algorithm doesn't care that y and x are actually log(F) and log(R) - it makes no difference to the algorithm.

The last step is a little bit different - and this is where you're getting tripped up in your plot above. What you're doing is computing

F = m R + b

but this is incorrect, because the relationship between F and R is not linear. (That's why you're using a log-log plot.)

Instead, you should compute

log(F) = m log(R) + b

If you transform this (raise 10 to the power of both sides and rearrange), you get

F = c R^m

where c = 10^b. This is the relationship between F and R: it is a power law relationship. (Power law relationships are what log-log plots are best at.)

In your code, you're using A and B when calling polyfit, but you should be using log(A) and log(B).

Answer 2

Your linear fit is not performed on the same data as shown in the loglog-plot.

Make a and b numpy arrays like this

a = numpy.asarray(a, dtype=float)
b = numpy.asarray(b, dtype=float)

Now you can perform operations on them. What the loglog-plot does, is to take the logarithm to base 10 of both a and b. You can do the same by

logA = numpy.log10(a)
logB = numpy.log10(b)

This is what the loglog plot visualizes. Check this by ploting both logA and logB as a regular plot. Repeat the linear fit on the log data and plot your line in the same plot as the logA, logB data.

coefficients = numpy.polyfit(logB, logA, 1)
polynomial = numpy.poly1d(coefficients)
ys = polynomial(b)
plt.plot(logB, logA)
plt.plot(b, ys)