Is there a python (scipy) function to determine parameters needed to obtain a target power?

Question

In R there is a very useful function that helps with determining parameters for a two sided t-test in order to obtain a target statistical power.

The function is called power.prop.test.

http://stat.ethz.ch/R-manual/R-patched/library/stats/html/power.prop.test.html

You can call it using:

power.prop.test(p1 = .50, p2 = .75, power = .90) 

And it will tell you n the sample size needed to obtain this power. This is extremely useful in deterring sample sizes for tests.

Is there a similar function in the scipy package?

Answer 1

I've managed to replicate the function using the below formula for n and the inverse survival function norm.isf from scipy.stats

from scipy.stats import norm, zscore  def sample_power_probtest(p1, p2, power=0.8, sig=0.05):     z = norm.isf([sig/2]) #two-sided t test     zp = -1 * norm.isf([power])      d = (p1-p2)     s =2*((p1+p2) /2)*(1-((p1+p2) /2))     n = s * ((zp + z)**2) / (d**2)     return int(round(n[0]))  def sample_power_difftest(d, s, power=0.8, sig=0.05):     z = norm.isf([sig/2])     zp = -1 * norm.isf([power])     n = s * ((zp + z)**2) / (d**2)     return int(round(n[0]))  if __name__ == '__main__':      n = sample_power_probtest(0.1, 0.11, power=0.8, sig=0.05)     print n  #14752      n = sample_power_difftest(0.1, 0.5, power=0.8, sig=0.05)     print n  #392 

Answer 2

Some of the basic power calculations are now available in statsmodels

http://statsmodels.sourceforge.net/devel/stats.html#power-and-sample-size-calculations http://jpktd.blogspot.ca/2013/03/statistical-power-in-statsmodels.html

The blog article does not yet take the latest changes to the statsmodels code into account. Also, I haven't decided yet how many wrapper functions to provide, since many power calculations just reduce to the basic distribution.

>>> 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 

In R stats

> 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  

using R's pwr package

> 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