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played with permutation test

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This commit is contained in:
Jan Benda 2017-12-04 19:15:09 +01:00
parent c54da6bf8f
commit e39f29847f
1 changed files with 10 additions and 2 deletions
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12
bootstrap/lecture/permutecorrelation.py
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@ -1,4 +1,5 @@
import numpy as np
import scipy.stats as st
import matplotlib.pyplot as plt
plt.xkcd()
@ -10,9 +11,10 @@ n = 200
a = 0.2
x = rng.randn(n);
y = rng.randn(n) + a*x;
#x = rng.exponential(1.0, n);
#y = rng.exponential(2.0, n) + a*x;
rd = np.corrcoef(x, y)[0, 1]
print rd
# permutation:
nperm = 1000
@ -26,7 +28,13 @@ for i in xrange(nperm) :
h, b = np.histogram(rs, 20, density=True)
# significance:
rq = np.percentile(rs, 95.0);
rq = np.percentile(rs, 95.0)
print('Measured correlation coefficient = %.2f, correlation coefficient at 95%% percentile of bootstrap = %.2f' % (rd, rq))
ra = 1.0-0.01*st.percentileofscore(rs, rd)
print('Measured correlation coefficient %.2f is at %.4f percentile of bootstrap' % (rd, ra))
rp, ra = st.pearsonr(x, y)
print('Measured correlation coefficient %.2f is at %.4f percentile of test' % (rp, ra))
ax = fig.add_subplot(1, 1, 1)
ax.spines['right'].set_visible(False)