M-Estimators for Robust Linear Modeling



In [ ]:

    
%matplotlib inline

from __future__ import print_function
from statsmodels.compat import lmap
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt

import statsmodels.api as sm

An M-estimator minimizes the function

$$Q(e_i, \rho) = \sum_i~\rho \left (\frac{e_i}{s}\right )$$

where $\rho$ is a symmetric function of the residuals

The effect of $\rho$ is to reduce the influence of outliers
$s$ is an estimate of scale.
The robust estimates $\hat{\beta}$ are computed by the iteratively re-weighted least squares algorithm

We have several choices available for the weighting functions to be used



In [ ]:

    
norms = sm.robust.norms



In [ ]:

    
def plot_weights(support, weights_func, xlabels, xticks):
    fig = plt.figure(figsize=(12,8))
    ax = fig.add_subplot(111)
    ax.plot(support, weights_func(support))
    ax.set_xticks(xticks)
    ax.set_xticklabels(xlabels, fontsize=16)
    ax.set_ylim(-.1, 1.1)
    return ax

Andrew's Wave



In [ ]:

    
help(norms.AndrewWave.weights)



In [ ]:

    
a = 1.339
support = np.linspace(-np.pi*a, np.pi*a, 100)
andrew = norms.AndrewWave(a=a)
plot_weights(support, andrew.weights, ['$-\pi*a$', '0', '$\pi*a$'], [-np.pi*a, 0, np.pi*a]);

Hampel's 17A



In [ ]:

    
help(norms.Hampel.weights)



In [ ]:

    
c = 8
support = np.linspace(-3*c, 3*c, 1000)
hampel = norms.Hampel(a=2., b=4., c=c)
plot_weights(support, hampel.weights, ['3*c', '0', '3*c'], [-3*c, 0, 3*c]);

Huber's t



In [ ]:

    
help(norms.HuberT.weights)



In [ ]:

    
t = 1.345
support = np.linspace(-3*t, 3*t, 1000)
huber = norms.HuberT(t=t)
plot_weights(support, huber.weights, ['-3*t', '0', '3*t'], [-3*t, 0, 3*t]);

Least Squares



In [ ]:

    
help(norms.LeastSquares.weights)



In [ ]:

    
support = np.linspace(-3, 3, 1000)
lst_sq = norms.LeastSquares()
plot_weights(support, lst_sq.weights, ['-3', '0', '3'], [-3, 0, 3]);

Ramsay's Ea



In [ ]:

    
help(norms.RamsayE.weights)



In [ ]:

    
a = .3
support = np.linspace(-3*a, 3*a, 1000)
ramsay = norms.RamsayE(a=a)
plot_weights(support, ramsay.weights, ['-3*a', '0', '3*a'], [-3*a, 0, 3*a]);

Trimmed Mean



In [ ]:

    
help(norms.TrimmedMean.weights)



In [ ]:

    
c = 2
support = np.linspace(-3*c, 3*c, 1000)
trimmed = norms.TrimmedMean(c=c)
plot_weights(support, trimmed.weights, ['-3*c', '0', '3*c'], [-3*c, 0, 3*c]);

Tukey's Biweight



In [ ]:

    
help(norms.TukeyBiweight.weights)



In [ ]:

    
c = 4.685
support = np.linspace(-3*c, 3*c, 1000)
tukey = norms.TukeyBiweight(c=c)
plot_weights(support, tukey.weights, ['-3*c', '0', '3*c'], [-3*c, 0, 3*c]);

Scale Estimators

Robust estimates of the location



In [ ]:

    
x = np.array([1, 2, 3, 4, 500])

The mean is not a robust estimator of location



In [ ]:

    
x.mean()

The median, on the other hand, is a robust estimator with a breakdown point of 50%



In [ ]:

    
np.median(x)

Analagously for the scale
The standard deviation is not robust



In [ ]:

    
x.std()

Median Absolute Deviation

$$ median_i |X_i - median_j(X_j)|) $$

Standardized Median Absolute Deviation is a consistent estimator for $\hat{\sigma}$

$$\hat{\sigma}=K \cdot MAD$$

where $K$ depends on the distribution. For the normal distribution for example,

$$K = \Phi^{-1}(.75)$$



In [ ]:

    
stats.norm.ppf(.75)



In [ ]:

    
print(x)



In [ ]:

    
sm.robust.scale.stand_mad(x)



In [ ]:

    
np.array([1,2,3,4,5.]).std()

The default for Robust Linear Models is MAD
another popular choice is Huber's proposal 2



In [ ]:

    
np.random.seed(12345)
fat_tails = stats.t(6).rvs(40)



In [ ]:

    
kde = sm.nonparametric.KDEUnivariate(fat_tails)
kde.fit()
fig = plt.figure(figsize=(12,8))
ax = fig.add_subplot(111)
ax.plot(kde.support, kde.density);



In [ ]:

    
print(fat_tails.mean(), fat_tails.std())



In [ ]:

    
print(stats.norm.fit(fat_tails))



In [ ]:

    
print(stats.t.fit(fat_tails, f0=6))



In [ ]:

    
huber = sm.robust.scale.Huber()
loc, scale = huber(fat_tails)
print(loc, scale)



In [ ]:

    
sm.robust.stand_mad(fat_tails)



In [ ]:

    
sm.robust.stand_mad(fat_tails, c=stats.t(6).ppf(.75))



In [ ]:

    
sm.robust.scale.mad(fat_tails)

Duncan's Occupational Prestige data - M-estimation for outliers



In [ ]:

    
from statsmodels.graphics.api import abline_plot
from statsmodels.formula.api import ols, rlm



In [ ]:

    
prestige = sm.datasets.get_rdataset("Duncan", "car", cache=True).data



In [ ]:

    
print(prestige.head(10))



In [ ]:

    
fig = plt.figure(figsize=(12,12))
ax1 = fig.add_subplot(211, xlabel='Income', ylabel='Prestige')
ax1.scatter(prestige.income, prestige.prestige)
xy_outlier = prestige.ix['minister'][['income','prestige']]
ax1.annotate('Minister', xy_outlier, xy_outlier+1, fontsize=16)
ax2 = fig.add_subplot(212, xlabel='Education',
                           ylabel='Prestige')
ax2.scatter(prestige.education, prestige.prestige);



In [ ]:

    
ols_model = ols('prestige ~ income + education', prestige).fit()
print(ols_model.summary())



In [ ]:

    
infl = ols_model.get_influence()
student = infl.summary_frame()['student_resid']
print(student)



In [ ]:

    
print(student.ix[np.abs(student) > 2])



In [ ]:

    
print(infl.summary_frame().ix['minister'])



In [ ]:

    
sidak = ols_model.outlier_test('sidak')
sidak.sort('unadj_p', inplace=True)
print(sidak)



In [ ]:

    
fdr = ols_model.outlier_test('fdr_bh')
fdr.sort('unadj_p', inplace=True)
print(fdr)



In [ ]:

    
rlm_model = rlm('prestige ~ income + education', prestige).fit()
print(rlm_model.summary())



In [ ]:

    
print(rlm_model.weights)

Hertzprung Russell data for Star Cluster CYG 0B1 - Leverage Points

Data is on the luminosity and temperature of 47 stars in the direction of Cygnus.



In [ ]:

    
dta = sm.datasets.get_rdataset("starsCYG", "robustbase", cache=True).data



In [ ]:

    
from matplotlib.patches import Ellipse
fig = plt.figure(figsize=(12,8))
ax = fig.add_subplot(111, xlabel='log(Temp)', ylabel='log(Light)', title='Hertzsprung-Russell Diagram of Star Cluster CYG OB1')
ax.scatter(*dta.values.T)
# highlight outliers
e = Ellipse((3.5, 6), .2, 1, alpha=.25, color='r')
ax.add_patch(e);
ax.annotate('Red giants', xy=(3.6, 6), xytext=(3.8, 6),
            arrowprops=dict(facecolor='black', shrink=0.05, width=2),
            horizontalalignment='left', verticalalignment='bottom',
            clip_on=True, # clip to the axes bounding box
            fontsize=16,
     )
# annotate these with their index
for i,row in dta.ix[dta['log.Te'] < 3.8].iterrows():
    ax.annotate(i, row, row + .01, fontsize=14)
xlim, ylim = ax.get_xlim(), ax.get_ylim()



In [ ]:

    
from IPython.display import Image
Image(filename='star_diagram.png')



In [ ]:

    
y = dta['log.light']
X = sm.add_constant(dta['log.Te'], prepend=True)
ols_model = sm.OLS(y, X).fit()
abline_plot(model_results=ols_model, ax=ax)



In [ ]:

    
rlm_mod = sm.RLM(y, X, sm.robust.norms.TrimmedMean(.5)).fit()
abline_plot(model_results=rlm_mod, ax=ax, color='red')

Why? Because M-estimators are not robust to leverage points.



In [ ]:

    
infl = ols_model.get_influence()



In [ ]:

    
h_bar = 2*(ols_model.df_model + 1 )/ols_model.nobs
hat_diag = infl.summary_frame()['hat_diag']
hat_diag.ix[hat_diag > h_bar]



In [ ]:

    
sidak2 = ols_model.outlier_test('sidak')
sidak2.sort('unadj_p', inplace=True)
print(sidak2)



In [ ]:

    
fdr2 = ols_model.outlier_test('fdr_bh')
fdr2.sort('unadj_p', inplace=True)
print(fdr2)

Let's delete that line



In [ ]:

    
del ax.lines[-1]



In [ ]:

    
weights = np.ones(len(X))
weights[X[X['log.Te'] < 3.8].index.values - 1] = 0
wls_model = sm.WLS(y, X, weights=weights).fit()
abline_plot(model_results=wls_model, ax=ax, color='green')

MM estimators are good for this type of problem, unfortunately, we don't yet have these yet.
It's being worked on, but it gives a good excuse to look at the R cell magics in the notebook.



In [ ]:

    
yy = y.values[:,None]
xx = X['log.Te'].values[:,None]



In [ ]:

    
%load_ext rpy2.ipython

%R library(robustbase)
%Rpush yy xx
%R mod <- lmrob(yy ~ xx);
%R params <- mod$coefficients;
%Rpull params



In [ ]:

    
%R print(mod)



In [ ]:

    
print(params)



In [ ]:

    
abline_plot(intercept=params[0], slope=params[1], ax=ax, color='green')

Exercise: Breakdown points of M-estimator



In [ ]:

    
np.random.seed(12345)
nobs = 200
beta_true = np.array([3, 1, 2.5, 3, -4])
X = np.random.uniform(-20,20, size=(nobs, len(beta_true)-1))
# stack a constant in front
X = sm.add_constant(X, prepend=True) # np.c_[np.ones(nobs), X]
mc_iter = 500
contaminate = .25 # percentage of response variables to contaminate



In [ ]:

    
all_betas = []
for i in range(mc_iter):
    y = np.dot(X, beta_true) + np.random.normal(size=200)
    random_idx = np.random.randint(0, nobs, size=int(contaminate * nobs))
    y[random_idx] = np.random.uniform(-750, 750)
    beta_hat = sm.RLM(y, X).fit().params
    all_betas.append(beta_hat)



In [ ]:

    
all_betas = np.asarray(all_betas)
se_loss = lambda x : np.linalg.norm(x, ord=2)**2
se_beta = lmap(se_loss, all_betas - beta_true)

Squared error loss



In [ ]:

    
np.array(se_beta).mean()



In [ ]:

    
all_betas.mean(0)



In [ ]:

    
beta_true



In [ ]:

    
se_loss(all_betas.mean(0) - beta_true)