Source code for statsmodels.sandbox.regression.anova_nistcertified

'''calculating anova and verifying with NIST test data

compares my implementations, stats.f_oneway and anova using statsmodels.OLS
from statsmodels.compat.python import lmap
import os
import numpy as np
from scipy import stats
from import add_constant
from statsmodels.regression.linear_model import OLS
from .try_ols_anova import data2dummy

filenameli = ['SiRstv.dat', 'SmLs01.dat', 'SmLs02.dat', 'SmLs03.dat', 'AtmWtAg.dat',
              'SmLs04.dat', 'SmLs05.dat', 'SmLs06.dat', 'SmLs07.dat', 'SmLs08.dat',
##filename = 'SmLs03.dat' #'SiRstv.dat' #'SmLs09.dat'#, 'AtmWtAg.dat' #'SmLs07.dat'

##path = __file__

def getnist(filename):
    here = os.path.dirname(__file__)
    fname = os.path.abspath(os.path.join(here, 'data', filename))
    with open(fname, 'r', encoding="utf-8") as fd:
        content ='\n')

    data = [line.split() for line in content[60:]]
    certified = [line.split() for line in content[40:48] if line]
    dataf = np.loadtxt(fname, skiprows=60)
    y,x = dataf.T
    y = y.astype(int)
    caty = np.unique(y)
    f = float(certified[0][-1])
    R2 = float(certified[2][-1])
    resstd = float(certified[4][-1])
    dfbn = int(certified[0][-4])
    dfwn = int(certified[1][-3])  # dfbn->dfwn is this correct
    prob = stats.f.sf(f,dfbn,dfwn)
    return y, x, np.array([f, prob, R2, resstd]), certified, caty

[docs]def anova_oneway(y, x, seq=0): # new version to match NIST # no generalization or checking of arguments, tested only for 1d yrvs = y[:,np.newaxis] #- min(y) #subracting mean increases numerical accuracy for NIST test data sets xrvs = x[:,np.newaxis] - x.mean() #for 1d#- 1e12 trick for 'SmLs09.dat' from .try_catdata import groupsstats_dummy meang, varg, xdevmeangr, countg = groupsstats_dummy(yrvs[:, :1], xrvs[:, :1]) # TODO: the following does not work as replacement # from .try_catdata import groupsstats_dummy, groupstatsbin # gcount, gmean , meanarr, withinvar, withinvararr = groupstatsbin(y, x) sswn =,xdevmeangr) ssbn =**2, countg.T) nobs = yrvs.shape[0] ncat = meang.shape[1] dfbn = ncat - 1 dfwn = nobs - ncat msb = ssbn/float(dfbn) msw = sswn/float(dfwn) f = msb/msw prob = stats.f.sf(f,dfbn,dfwn) R2 = (ssbn/(sswn+ssbn)) #R-squared resstd = np.sqrt(msw) #residual standard deviation #print(f, prob def _fix2scalar(z): # return number if np.shape(z) == (1, 1): return z[0, 0] else: return z f, prob, R2, resstd = lmap(_fix2scalar, (f, prob, R2, resstd)) return f, prob, R2, resstd
[docs]def anova_ols(y, x): X = add_constant(data2dummy(x), prepend=False) res = OLS(y, X).fit() return res.fvalue, res.f_pvalue, res.rsquared, np.sqrt(res.mse_resid)
if __name__ == '__main__': print('\n using new ANOVA anova_oneway') print('f, prob, R2, resstd') for fn in filenameli: print(fn) y, x, cert, certified, caty = getnist(fn) res = anova_oneway(y, x) # TODO: figure out why these results are less accurate/precise # than others rtol = { "SmLs08.dat": .027, "SmLs07.dat": 1.7e-3, "SmLs09.dat": 1e-4 }.get(fn, 1e-7) np.testing.assert_allclose(np.array(res), cert, rtol=rtol) print('\n using stats ANOVA f_oneway') for fn in filenameli: print(fn) y, x, cert, certified, caty = getnist(fn) xlist = [x[y==ii] for ii in caty] res = stats.f_oneway(*xlist) print(np.array(res) - cert[:2]) print('\n using statsmodels.OLS') print('f, prob, R2, resstd') for fn in filenameli[:]: print(fn) y, x, cert, certified, caty = getnist(fn) res = anova_ols(x, y) print(np.array(res) - cert)

Last update: May 05, 2023