<p>一些基本的功率计算现在可以在statsmodels中获得</p>
<p><a href="http://statsmodels.sourceforge.net/devel/stats.html#power-and-sample-size-calculations" rel="noreferrer">http://statsmodels.sourceforge.net/devel/stats.html#power-and-sample-size-calculations</a>
<a href="http://jpktd.blogspot.ca/2013/03/statistical-power-in-statsmodels.html" rel="noreferrer">http://jpktd.blogspot.ca/2013/03/statistical-power-in-statsmodels.html</a></p>
<p>博客文章还没有考虑到statsmodels代码的最新更改。另外,我还没有决定要提供多少包装器函数,因为许多功率计算只是减少到基本分布。</p>
<pre><code>>>> import statsmodels.stats.api as sms
>>> es = sms.proportion_effectsize(0.5, 0.75)
>>> sms.NormalIndPower().solve_power(es, power=0.9, alpha=0.05, ratio=1)
76.652940372066908
</code></pre>
<p>在R统计中</p>
<pre><code>> power.prop.test(p1 = .50, p2 = .75, power = .90)
Two-sample comparison of proportions power calculation
n = 76.7069301141077
p1 = 0.5
p2 = 0.75
sig.level = 0.05
power = 0.9
alternative = two.sided
NOTE: n is number in *each* group
</code></pre>
<p>使用R的<code>pwr</code>包</p>
<pre><code>> library(pwr)
> h<-ES.h(0.5,0.75)
> pwr.2p.test(h=h, power=0.9, sig.level=0.05)
Difference of proportion power calculation for binomial distribution (arcsine transformation)
h = 0.5235987755982985
n = 76.6529406106181
sig.level = 0.05
power = 0.9
alternative = two.sided
NOTE: same sample sizes
</code></pre>