[Power analysis with python] #python

power-analysis.py

# estimate sample size via power analysis
from statsmodels.stats.power import TTestIndPower

# parameters for power analysis
effect = 0.8
alpha = 0.05
power = 0.8

# perform power analysis
analysis = TTestIndPower()
result = analysis.solve_power(effect, power=power, nobs1=None, ratio=1.0, alpha=alpha)
print('Sample Size: %.3f' % result)


# calculate power curves for varying sample and effect size
from numpy import array
from matplotlib import pyplot
from statsmodels.stats.power import TTestIndPower
# parameters for power analysis
effect_sizes = array([0.2, 0.5, 0.8])
sample_sizes = array(range(5, 100))
# calculate power curves from multiple power analyses
analysis = TTestIndPower()
analysis.plot_power(dep_var='nobs', nobs=sample_sizes, effect_size=effect_sizes)
pyplot.show()