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:[email protected]]
> Sent: Friday, May 21, 2010 6:37 AM
> To: [email protected]
> 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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