qqplot

Community matplotlib-users
1

hi all

has anyone ever tried to make a quantile-quantile plot with pylab?
is there any build in function named say "qqplot" available ?

thanks
Thierry

2

Thierry,

You need either scipy or rpy2 (and R) to do this. I've attached some code below. Please keep in mind that I've written for the general case of having a censored data set, therefore I rely on masked arrays from numpy.ma and scipy.stats.mstats -- but I have apply the mask midway through the process, which is different than the numpy's standard operating procedure. Let me know if any of this isn't clear.

I also have code that generates a quick comparison of the results from scipy.stats.mstats and ryp2+R, if you're interested.

HTH,
-paul

# code...
import matplotlib.pyplot as pl
import scipy.stats as st
import numpy as np

def censoredProbPlot(data, mask):
    ppos = st.mstats.plotting_positions(data)
    qntl = st.distributions.norm.ppf(ppos)
    
    qntlMask = np.ma.MaskedArray(qntl, mask=mask)
    dataMask = np.ma.MaskedArray(data, mask=mask)
    
    fit = st.mstats.linregress(dataMask, qntlMask)
    mu = -fit[1]
    sigma = fit[0]
    d_ = np.linspace(np.min(data),np.max(data))
    q_ = sigma * d_ - mu
    
    maskedProbPlot = {"mskData" : dataMask,
                      "mskQntl" : qntlMask,
                      "unmskData" : data,
                      "unmskQntl" : qntl,
                      "bestFitD" : d_,
                      "bestFitQ" : q_,
                      "mu" : mu,
                      "sigma" : sigma}
    return maskedProbPlot

if 1:
    #~~ you need to put your data here:
    #data = np.array()
    #mask = np.array(,dtype=bool)

    mpp = censoredProbPlot(data, mask)

    fig = pl.figure()
    ax1 = fig.add_subplot(111)
    ax1.plot(mpp['mskQntl'], mpp['mskData'], 'ko', ms=6, label='Detected Samples')
    ax1.plot(mpp['unmskQntl'], mpp['unmskData'], 'r.', ms=6, label='Raw Samples')
    ax1.plot(mpp['bestFitQ'], mpp['bestFitD'], 'b-', lw=2)
    fig.savefig('example_censoredProbPlot.png')

···

-----Original Message-----
From: MONTAGU Thierry [mailto:thierry.montagu@…3117…]
Sent: Friday, May 21, 2010 6:37 AM
To: matplotlib-users@lists.sourceforge.net
Subject: [Matplotlib-users] qqplot

hi all

has anyone ever tried to make a quantile-quantile plot with pylab?
is there any build in function named say "qqplot" available ?

thanks
Thierry

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3

From: MONTAGU Thierry [mailto:thierry.montagu@…3117…]
Sent: Friday, May 21, 2010 09:37

has anyone ever tried to make a quantile-quantile plot with pylab?
is there any build in function named say "qqplot" available ?

For a plot comparing samples to a theoretical distribution (and if you don't
need masking as in Paul's example), you might be able to use
scipy.stats.probplot, as follows:

    import matplotlib.pyplot as plt
    import scipy.stats as st

    values = st.norm.rvs(size=(100,)) # example data
    fig = plt.figure() # set up plot
    ax = fig.add_subplot(1, 1, 1)
    osm, osr = st.probplot(values, fit=0, dist='norm') # compute
    ax.plot(osm, osr, '.') # plot

One way to include the fit line is

    (osm, osr), (m, b, r) = st.probplot(values, dist='norm') # compute
    osmf = osm.take([0, -1]) # endpoints
    osrf = m * osmf + b # fit line
    ax.plot(osm, osr, '.', osmf, osrf, '-')