6.10.5.2.1. statsmodels.sandbox.tools.try_mctools.StatTestMC¶
-
class
statsmodels.sandbox.tools.try_mctools.
StatTestMC
(dgp, statistic)[source]¶ class to run Monte Carlo study on a statistical test’‘’
TODO print(summary, for quantiles and for histogram draft in trying out script log
Parameters: dgp : callable
Function that generates the data to be used in Monte Carlo that should return a new sample with each call
statistic : callable
Function that calculates the test statistic, which can return either a single statistic or a 1d array_like (tuple, list, ndarray). see also statindices in description of run
Notes
Warning
This is (currently) designed for a single call to run. If run is called a second time with different arguments, then some attributes might not be updated, and, therefore, not correspond to the same run.
Warning
Under Construction, don’t expect stability in Api or implementation
Examples
Define a function that defines our test statistic:
- def lb(x):
- s,p = acorr_ljungbox(x, lags=4) return np.r_[s, p]
Note lb returns eight values.
Define a random sample generator, for example 500 independently, normal distributed observations in a sample:
- def normalnoisesim(nobs=500, loc=0.0):
- return (loc+np.random.randn(nobs))
Create instance and run Monte Carlo. Using statindices=list(range(4)) means that only the first for values of the return of the statistic (lb) are stored in the Monte Carlo results.
mc1 = StatTestMC(normalnoisesim, lb) mc1.run(5000, statindices=list(range(4)))
Most of the other methods take an idx which indicates for which columns the results should be presented, e.g.
print(mc1.cdf(crit, [1,2,3])[1]
Attributes
many methods store intermediate results self.mcres (ndarray (nrepl, nreturns) or (nrepl, len(statindices))) Monte Carlo results stored by run
6.10.5.2.1.1. Methods¶
__init__ (dgp, statistic) |
|
cdf (x[, idx]) |
calculate cumulative probabilities of Monte Carlo results |
get_mc_sorted () |
|
histogram ([idx, critval]) |
calculate histogram values |
plot_hist (idx[, distpdf, bins, ax, kwds]) |
plot the histogram against a reference distribution |
quantiles ([idx, frac]) |
calculate quantiles of Monte Carlo results |
run (nrepl[, statindices, dgpargs, statsargs]) |
run the actual Monte Carlo and save results |
summary_cdf (idx, frac, crit[, varnames, title]) |
summary table for cumulative density function |
summary_quantiles (idx, distppf[, frac, ...]) |
summary table for quantiles (critical values) |