48 lines
1.5 KiB
Python
48 lines
1.5 KiB
Python
import numpy as np
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import matplotlib.pyplot as plt
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from plotstyle import *
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rng = np.random.RandomState(637281)
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nsamples = 100
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nresamples = 1000
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db = 0.05;
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bins = np.arange(-0.4, 0.41, db)
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# draw a SRS (simple random sample, "Stichprobe") from the population:
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x = rng.randn(nsamples)
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# bootstrap the mean:
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mus = []
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for i in range(nresamples) :
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mus.append(np.mean(x[rng.randint(0, nsamples, nsamples)]))
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hmus, _ = np.histogram(mus, bins, density=True)
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# many SRS:
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musrs = []
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for i in range(nresamples) :
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musrs.append(np.mean(rng.randn(nsamples)))
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hmusrs, _ = np.histogram(musrs, bins, density=True)
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fig, ax = plt.subplots(figsize=cm_size(figure_width, 1.1*figure_height))
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fig.subplots_adjust(**adjust_fs(left=4.0, bottom=2.7, right=1.5))
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ax.set_xlabel('Mean')
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ax.set_xlim(-0.4, 0.4)
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ax.set_ylabel('Probability density')
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ax.set_ylim(0.0, 4.5)
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ax.set_yticks(np.arange(0.0, 4.5, 1.0))
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ax.annotate('sampling\ndistribution',
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xy=(-0.12, 3.1), xycoords='data',
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xytext=(-0.18, 3.5), textcoords='data', ha='right',
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arrowprops=dict(arrowstyle="->", relpos=(1.0,0.5),
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connectionstyle="angle3,angleA=20,angleB=120") )
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ax.annotate('bootstrap\ndistribution',
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xy=(0.13, 3.3), xycoords='data',
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xytext=(0.22, 4), textcoords='data',
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arrowprops=dict(arrowstyle="->", relpos=(0.0,0.5),
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connectionstyle="angle3,angleA=20,angleB=60") )
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ax.bar(bins[:-1]-0.25*db, hmusrs, 0.5*db, **fsB)
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ax.bar(bins[:-1]+0.25*db, hmus, 0.5*db, **fsA)
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plt.savefig('bootstrapsem.pdf')
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