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Nicolas P. Rougier 2016-07-14 17:28:35 -05:00
parent 50b73978a4
commit 9465ae21ad
2 changed files with 13 additions and 9 deletions
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README.html
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@ -955,7 +955,9 @@ the rows which only contain integers and which sum to n. (★★★)</p>
<span class="keyword">print</span><span class="punctuation">(</span><span class="name">X</span><span class="punctuation">[</span><span class="name">M</span><span class="punctuation">])</span> <span class="keyword">print</span><span class="punctuation">(</span><span class="name">X</span><span class="punctuation">[</span><span class="name">M</span><span class="punctuation">])</span>
</pre> </pre>
</li> </li>
<li><p class="first">Compute bootstrapped 95% confidence intervals for the mean of a 1D array X (i.e., resample the elements of an array with replacement N times, compute the mean of each sample, and then compute percentiles over the means). (★★★)</p> <li><p class="first">Compute bootstrapped 95% confidence intervals for the mean of a 1D array X
(i.e., resample the elements of an array with replacement N times, compute
the mean of each sample, and then compute percentiles over the means). (★★★)</p>
</li> </li>
</ol> </ol>
<blockquote> <blockquote>

18
README.rst
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@ -1114,15 +1114,17 @@ Thanks to Michiaki Ariga, there is now a
M &= (X.sum(axis=-1) == n) M &= (X.sum(axis=-1) == n)
print(X[M]) print(X[M])
#. Compute bootstrapped 95% confidence intervals for the mean of a 1D array X (i.e., resample the elements of an array with replacement N times, compute the mean of each sample, and then compute percentiles over the means). (★★★) #. Compute bootstrapped 95% confidence intervals for the mean of a 1D array X
(i.e., resample the elements of an array with replacement N times, compute
the mean of each sample, and then compute percentiles over the means). (★★★)
.. code-block:: python .. code-block:: python
# Author: Jessica B. Hamrick # Author: Jessica B. Hamrick
X = np.random.randn(100) # random 1D array X = np.random.randn(100) # random 1D array
N = 1000 # number of bootstrap samples N = 1000 # number of bootstrap samples
idx = np.random.randint(0, X.size, (N, X.size)) idx = np.random.randint(0, X.size, (N, X.size))
means = X[idx].mean(axis=1) means = X[idx].mean(axis=1)
confint = np.percentile(means, [2.5, 97.5]) confint = np.percentile(means, [2.5, 97.5])
print(confint) print(confint)