Source code for statsmodels.stats.contrast
import numpy as np
from scipy.stats import f as fdist
from scipy.stats import t as student_t
from scipy import stats
from statsmodels.tools.tools import clean0, fullrank
from statsmodels.stats.multitest import multipletests
#TODO: should this be public if it's just a container?
[docs]
class ContrastResults:
"""
Class for results of tests of linear restrictions on coefficients in a model.
This class functions mainly as a container for `t_test`, `f_test` and
`wald_test` for the parameters of a model.
The attributes depend on the statistical test and are either based on the
normal, the t, the F or the chisquare distribution.
"""
def __init__(self, t=None, F=None, sd=None, effect=None, df_denom=None,
df_num=None, alpha=0.05, **kwds):
self.effect = effect # Let it be None for F
if F is not None:
self.distribution = 'F'
self.fvalue = F
self.statistic = self.fvalue
self.df_denom = df_denom
self.df_num = df_num
self.dist = fdist
self.dist_args = (df_num, df_denom)
self.pvalue = fdist.sf(F, df_num, df_denom)
elif t is not None:
self.distribution = 't'
self.tvalue = t
self.statistic = t # generic alias
self.sd = sd
self.df_denom = df_denom
self.dist = student_t
self.dist_args = (df_denom,)
self.pvalue = self.dist.sf(np.abs(t), df_denom) * 2
elif 'statistic' in kwds:
# TODO: currently targeted to normal distribution, and chi2
self.distribution = kwds['distribution']
self.statistic = kwds['statistic']
self.tvalue = value = kwds['statistic'] # keep alias
# TODO: for results instance we decided to use tvalues also for normal
self.sd = sd
self.dist = getattr(stats, self.distribution)
self.dist_args = kwds.get('dist_args', ())
if self.distribution == 'chi2':
self.pvalue = self.dist.sf(self.statistic, df_denom)
self.df_denom = df_denom
else:
"normal"
self.pvalue = np.full_like(value, np.nan)
not_nan = ~np.isnan(value)
self.pvalue[not_nan] = self.dist.sf(np.abs(value[not_nan])) * 2
else:
self.pvalue = np.nan
# cleanup
# should we return python scalar?
self.pvalue = np.squeeze(self.pvalue)
if self.effect is not None:
self.c_names = ['c%d' % ii for ii in range(len(self.effect))]
else:
self.c_names = None
[docs]
def conf_int(self, alpha=0.05):
"""
Returns the confidence interval of the value, `effect` of the constraint.
This is currently only available for t and z tests.
Parameters
----------
alpha : float, optional
The significance level for the confidence interval.
ie., The default `alpha` = .05 returns a 95% confidence interval.
Returns
-------
ci : ndarray, (k_constraints, 2)
The array has the lower and the upper limit of the confidence
interval in the columns.
"""
if self.effect is not None:
# confidence intervals
q = self.dist.ppf(1 - alpha / 2., *self.dist_args)
lower = self.effect - q * self.sd
upper = self.effect + q * self.sd
return np.column_stack((lower, upper))
else:
raise NotImplementedError('Confidence Interval not available')
def __str__(self):
return self.summary().__str__()
def __repr__(self):
return str(self.__class__) + '\n' + self.__str__()
[docs]
def summary(self, xname=None, alpha=0.05, title=None):
"""Summarize the Results of the hypothesis test
Parameters
----------
xname : list[str], optional
Default is `c_##` for ## in the number of regressors
alpha : float
significance level for the confidence intervals. Default is
alpha = 0.05 which implies a confidence level of 95%.
title : str, optional
Title for the params table. If not None, then this replaces the
default title
Returns
-------
smry : str or Summary instance
This contains a parameter results table in the case of t or z test
in the same form as the parameter results table in the model
results summary.
For F or Wald test, the return is a string.
"""
if self.effect is not None:
# TODO: should also add some extra information, e.g. robust cov ?
# TODO: can we infer names for constraints, xname in __init__ ?
if title is None:
title = 'Test for Constraints'
elif title == '':
# do not add any title,
# I think SimpleTable skips on None - check
title = None
# we have everything for a params table
use_t = (self.distribution == 't')
yname='constraints' # Not used in params_frame
if xname is None:
xname = self.c_names
from statsmodels.iolib.summary import summary_params
pvalues = np.atleast_1d(self.pvalue)
summ = summary_params((self, self.effect, self.sd, self.statistic,
pvalues, self.conf_int(alpha)),
yname=yname, xname=xname, use_t=use_t,
title=title, alpha=alpha)
return summ
elif hasattr(self, 'fvalue'):
# TODO: create something nicer for these casee
return ('<F test: F=%s, p=%s, df_denom=%.3g, df_num=%.3g>' %
(repr(self.fvalue), self.pvalue, self.df_denom,
self.df_num))
elif self.distribution == 'chi2':
return ('<Wald test (%s): statistic=%s, p-value=%s, df_denom=%.3g>' %
(self.distribution, self.statistic, self.pvalue,
self.df_denom))
else:
# generic
return ('<Wald test: statistic=%s, p-value=%s>' %
(self.statistic, self.pvalue))
[docs]
def summary_frame(self, xname=None, alpha=0.05):
"""Return the parameter table as a pandas DataFrame
This is only available for t and normal tests
"""
if self.effect is not None:
# we have everything for a params table
use_t = (self.distribution == 't')
yname='constraints' # Not used in params_frame
if xname is None:
xname = self.c_names
from statsmodels.iolib.summary import summary_params_frame
summ = summary_params_frame((self, self.effect, self.sd,
self.statistic,self.pvalue,
self.conf_int(alpha)), yname=yname,
xname=xname, use_t=use_t,
alpha=alpha)
return summ
else:
# TODO: create something nicer
raise NotImplementedError('only available for t and z')
class Contrast:
"""
This class is used to construct contrast matrices in regression models.
They are specified by a (term, design) pair. The term, T, is a linear
combination of columns of the design matrix. The matrix attribute of
Contrast is a contrast matrix C so that
colspan(dot(D, C)) = colspan(dot(D, dot(pinv(D), T)))
where pinv(D) is the generalized inverse of D. Further, the matrix
Tnew = dot(C, D)
is full rank. The rank attribute is the rank of
dot(D, dot(pinv(D), T))
In a regression model, the contrast tests that E(dot(Tnew, Y)) = 0
for each column of Tnew.
Parameters
----------
term : array_like
design : array_like
Attributes
----------
contrast_matrix
Examples
--------
>>> import statsmodels.api as sm
>>> from statsmodels.stats.contrast import Contrast
>>> import numpy as np
>>> np.random.seed(54321)
>>> X = np.random.standard_normal((40,10))
# Get a contrast
>>> new_term = np.column_stack((X[:,0], X[:,2]))
>>> c = Contrast(new_term, X)
>>> test = [[1] + [0]*9, [0]*2 + [1] + [0]*7]
>>> np.allclose(c.contrast_matrix, test)
True
Get another contrast
>>> P = np.dot(X, np.linalg.pinv(X))
>>> resid = np.identity(40) - P
>>> noise = np.dot(resid,np.random.standard_normal((40,5)))
>>> new_term2 = np.column_stack((noise,X[:,2]))
>>> c2 = Contrast(new_term2, X)
>>> print(c2.contrast_matrix)
[ -1.26424750e-16 8.59467391e-17 1.56384718e-01 -2.60875560e-17
-7.77260726e-17 -8.41929574e-18 -7.36359622e-17 -1.39760860e-16
1.82976904e-16 -3.75277947e-18]
Get another contrast
>>> zero = np.zeros((40,))
>>> new_term3 = np.column_stack((zero,X[:,2]))
>>> c3 = Contrast(new_term3, X)
>>> test2 = [0]*2 + [1] + [0]*7
>>> np.allclose(c3.contrast_matrix, test2)
True
"""
def _get_matrix(self):
"""
Gets the contrast_matrix property
"""
if not hasattr(self, "_contrast_matrix"):
self.compute_matrix()
return self._contrast_matrix
contrast_matrix = property(_get_matrix)
def __init__(self, term, design):
self.term = np.asarray(term)
self.design = np.asarray(design)
def compute_matrix(self):
"""
Construct a contrast matrix C so that
colspan(dot(D, C)) = colspan(dot(D, dot(pinv(D), T)))
where pinv(D) is the generalized inverse of D=design.
"""
T = self.term
if T.ndim == 1:
T = T[:,None]
self.T = clean0(T)
self.D = self.design
self._contrast_matrix = contrastfromcols(self.T, self.D)
try:
self.rank = self.matrix.shape[1]
except:
self.rank = 1
#TODO: fix docstring after usage is settled
def contrastfromcols(L, D, pseudo=None):
"""
From an n x p design matrix D and a matrix L, tries
to determine a p x q contrast matrix C which
determines a contrast of full rank, i.e. the
n x q matrix
dot(transpose(C), pinv(D))
is full rank.
L must satisfy either L.shape[0] == n or L.shape[1] == p.
If L.shape[0] == n, then L is thought of as representing
columns in the column space of D.
If L.shape[1] == p, then L is thought of as what is known
as a contrast matrix. In this case, this function returns an estimable
contrast corresponding to the dot(D, L.T)
Note that this always produces a meaningful contrast, not always
with the intended properties because q is always non-zero unless
L is identically 0. That is, it produces a contrast that spans
the column space of L (after projection onto the column space of D).
Parameters
----------
L : array_like
D : array_like
"""
L = np.asarray(L)
D = np.asarray(D)
n, p = D.shape
if L.shape[0] != n and L.shape[1] != p:
raise ValueError("shape of L and D mismatched")
if pseudo is None:
pseudo = np.linalg.pinv(D) # D^+ \approx= ((dot(D.T,D))^(-1),D.T)
if L.shape[0] == n:
C = np.dot(pseudo, L).T
else:
C = L
C = np.dot(pseudo, np.dot(D, C.T)).T
Lp = np.dot(D, C.T)
if len(Lp.shape) == 1:
Lp.shape = (n, 1)
if np.linalg.matrix_rank(Lp) != Lp.shape[1]:
Lp = fullrank(Lp)
C = np.dot(pseudo, Lp).T
return np.squeeze(C)
# TODO: this is currently a minimal version, stub
class WaldTestResults:
# for F and chi2 tests of joint hypothesis, mainly for vectorized
def __init__(self, statistic, distribution, dist_args, table=None,
pvalues=None):
self.table = table
self.distribution = distribution
self.statistic = statistic
#self.sd = sd
self.dist_args = dist_args
# The following is because I do not know which we want
if table is not None:
self.statistic = table['statistic'].values
self.pvalues = table['pvalue'].values
self.df_constraints = table['df_constraint'].values
if self.distribution == 'F':
self.df_denom = table['df_denom'].values
else:
if self.distribution == 'chi2':
self.dist = stats.chi2
self.df_constraints = self.dist_args[0] # assumes tuple
# using dist_args[0] is a bit dangerous,
elif self.distribution == 'F':
self.dist = stats.f
self.df_constraints, self.df_denom = self.dist_args
else:
raise ValueError('only F and chi2 are possible distribution')
if pvalues is None:
self.pvalues = self.dist.sf(np.abs(statistic), *dist_args)
else:
self.pvalues = pvalues
@property
def col_names(self):
"""column names for summary table
"""
pr_test = "P>%s" % self.distribution
col_names = [self.distribution, pr_test, 'df constraint']
if self.distribution == 'F':
col_names.append('df denom')
return col_names
def summary_frame(self):
# needs to be a method for consistency
if hasattr(self, '_dframe'):
return self._dframe
# rename the column nambes, but do not copy data
renaming = dict(zip(self.table.columns, self.col_names))
self.dframe = self.table.rename(columns=renaming)
return self.dframe
def __str__(self):
return self.summary_frame().to_string()
def __repr__(self):
return str(self.__class__) + '\n' + self.__str__()
# t_test for pairwise comparison and automatic contrast/restrictions
def _get_pairs_labels(k_level, level_names):
"""helper function for labels for pairwise comparisons
"""
idx_pairs_all = np.triu_indices(k_level, 1)
labels = [f'{level_names[name[1]]}-{level_names[name[0]]}'
for name in zip(*idx_pairs_all)]
return labels
def _contrast_pairs(k_params, k_level, idx_start):
"""create pairwise contrast for reference coding
currently not used,
using encoding contrast matrix is more general, but requires requires
factor information from patsy design_info.
Parameters
----------
k_params : int
number of parameters
k_level : int
number of levels or categories (including reference case)
idx_start : int
Index of the first parameter of this factor. The restrictions on the
factor are inserted as a block in the full restriction matrix starting
at column with index `idx_start`.
Returns
-------
contrasts : ndarray
restriction matrix with k_params columns and number of rows equal to
the number of restrictions.
"""
k_level_m1 = k_level - 1
idx_pairs = np.triu_indices(k_level_m1, 1)
k = len(idx_pairs[0])
c_pairs = np.zeros((k, k_level_m1))
c_pairs[np.arange(k), idx_pairs[0]] = -1
c_pairs[np.arange(k), idx_pairs[1]] = 1
c_reference = np.eye(k_level_m1)
c = np.concatenate((c_reference, c_pairs), axis=0)
k_all = c.shape[0]
contrasts = np.zeros((k_all, k_params))
contrasts[:, idx_start : idx_start + k_level_m1] = c
return contrasts
def t_test_multi(result, contrasts, method='hs', alpha=0.05, ci_method=None,
contrast_names=None):
"""perform t_test and add multiplicity correction to results dataframe
Parameters
----------
result results instance
results of an estimated model
contrasts : ndarray
restriction matrix for t_test
method : str or list of strings
method for multiple testing p-value correction, default is'hs'.
alpha : float
significance level for multiple testing reject decision.
ci_method : None
not used yet, will be for multiplicity corrected confidence intervals
contrast_names : {list[str], None}
If contrast_names are provided, then they are used in the index of the
returned dataframe, otherwise some generic default names are created.
Returns
-------
res_df : pandas DataFrame
The dataframe contains the results of the t_test and additional columns
for multiplicity corrected p-values and boolean indicator for whether
the Null hypothesis is rejected.
"""
tt = result.t_test(contrasts)
res_df = tt.summary_frame(xname=contrast_names)
if type(method) is not list:
method = [method]
for meth in method:
mt = multipletests(tt.pvalue, method=meth, alpha=alpha)
res_df['pvalue-%s' % meth] = mt[1]
res_df['reject-%s' % meth] = mt[0]
return res_df
class MultiCompResult:
"""class to hold return of t_test_pairwise
currently just a minimal class to hold attributes.
"""
def __init__(self, **kwargs):
self.__dict__.update(kwargs)
def _embed_constraints(contrasts, k_params, idx_start, index=None):
"""helper function to expand constraints to a full restriction matrix
Parameters
----------
contrasts : ndarray
restriction matrix for t_test
k_params : int
number of parameters
idx_start : int
Index of the first parameter of this factor. The restrictions on the
factor are inserted as a block in the full restriction matrix starting
at column with index `idx_start`.
index : slice or ndarray
Column index if constraints do not form a block in the full restriction
matrix, i.e. if parameters that are subject to restrictions are not
consecutive in the list of parameters.
If index is not None, then idx_start is ignored.
Returns
-------
contrasts : ndarray
restriction matrix with k_params columns and number of rows equal to
the number of restrictions.
"""
k_c, k_p = contrasts.shape
c = np.zeros((k_c, k_params))
if index is None:
c[:, idx_start : idx_start + k_p] = contrasts
else:
c[:, index] = contrasts
return c
def _constraints_factor(encoding_matrix, comparison='pairwise', k_params=None,
idx_start=None):
"""helper function to create constraints based on encoding matrix
Parameters
----------
encoding_matrix : ndarray
contrast matrix for the encoding of a factor as defined by patsy.
The number of rows should be equal to the number of levels or categories
of the factor, the number of columns should be equal to the number
of parameters for this factor.
comparison : str
Currently only 'pairwise' is implemented. The restriction matrix
can be used for testing the hypothesis that all pairwise differences
are zero.
k_params : int
number of parameters
idx_start : int
Index of the first parameter of this factor. The restrictions on the
factor are inserted as a block in the full restriction matrix starting
at column with index `idx_start`.
Returns
-------
contrast : ndarray
Contrast or restriction matrix that can be used in hypothesis test
of model results. The number of columns is k_params.
"""
cm = encoding_matrix
k_level, k_p = cm.shape
import statsmodels.sandbox.stats.multicomp as mc
if comparison in ['pairwise', 'pw', 'pairs']:
c_all = -mc.contrast_allpairs(k_level)
else:
raise NotImplementedError('currentlyonly pairwise comparison')
contrasts = c_all.dot(cm)
if k_params is not None:
if idx_start is None:
raise ValueError("if k_params is not None, then idx_start is "
"required")
contrasts = _embed_constraints(contrasts, k_params, idx_start)
return contrasts
def t_test_pairwise(result, term_name, method='hs', alpha=0.05,
factor_labels=None, ignore=False):
"""
Perform pairwise t_test with multiple testing corrected p-values.
This uses the formula design_info encoding contrast matrix and should
work for all encodings of a main effect.
Parameters
----------
result : result instance
The results of an estimated model with a categorical main effect.
term_name : str
name of the term for which pairwise comparisons are computed.
Term names for categorical effects are created by patsy and
correspond to the main part of the exog names.
method : {str, list[str]}
multiple testing p-value correction, default is 'hs',
see stats.multipletesting
alpha : float
significance level for multiple testing reject decision.
factor_labels : {list[str], None}
Labels for the factor levels used for pairwise labels. If not
provided, then the labels from the formula design_info are used.
ignore : bool
Turn off some of the exceptions raised by input checks.
Returns
-------
MultiCompResult
The results are stored as attributes, the main attributes are the
following two. Other attributes are added for debugging purposes
or as background information.
- result_frame : pandas DataFrame with t_test results and multiple
testing corrected p-values.
- contrasts : matrix of constraints of the null hypothesis in the
t_test.
Notes
-----
Status: experimental. Currently only checked for treatment coding with
and without specified reference level.
Currently there are no multiple testing corrected confidence intervals
available.
"""
desinfo = result.model.data.design_info
term_idx = desinfo.term_names.index(term_name)
term = desinfo.terms[term_idx]
idx_start = desinfo.term_slices[term].start
if not ignore and len(term.factors) > 1:
raise ValueError('interaction effects not yet supported')
factor = term.factors[0]
cat = desinfo.factor_infos[factor].categories
if factor_labels is not None:
if len(factor_labels) == len(cat):
cat = factor_labels
else:
raise ValueError("factor_labels has the wrong length, should be %d" % len(cat))
k_level = len(cat)
cm = desinfo.term_codings[term][0].contrast_matrices[factor].matrix
k_params = len(result.params)
labels = _get_pairs_labels(k_level, cat)
import statsmodels.sandbox.stats.multicomp as mc
c_all_pairs = -mc.contrast_allpairs(k_level)
contrasts_sub = c_all_pairs.dot(cm)
contrasts = _embed_constraints(contrasts_sub, k_params, idx_start)
res_df = t_test_multi(result, contrasts, method=method, ci_method=None,
alpha=alpha, contrast_names=labels)
res = MultiCompResult(result_frame=res_df,
contrasts=contrasts,
term=term,
contrast_labels=labels,
term_encoding_matrix=cm)
return res
def _offset_constraint(r_matrix, params_est, params_alt):
"""offset to the value of a linear constraint for new params
usage:
(cm, v) is original constraint
vo = offset_constraint(cm, res2.params, params_alt)
fs = res2.wald_test((cm, v + vo))
nc = fs.statistic * fs.df_num
"""
diff_est = r_matrix @ params_est
diff_alt = r_matrix @ params_alt
return diff_est - diff_alt
def wald_test_noncent(params, r_matrix, value, results, diff=None, joint=True):
"""Moncentrality parameter for a wald test in model results
The null hypothesis is ``diff = r_matrix @ params - value = 0``
Parameters
----------
params : ndarray
parameters of the model at which to evaluate noncentrality. This can
be estimated parameters or parameters under an alternative.
r_matrix : ndarray
Restriction matrix or contrasts for the Null hypothesis
value : None or ndarray
Value of the linear combination of parameters under the null
hypothesis. If value is None, then it will be replaced by zero.
results : Results instance of a model
The results instance is used to compute the covariance matrix of the
linear constraints using `cov_params.
diff : None or ndarray
If diff is not None, then it will be used instead of
``diff = r_matrix @ params - value``
joint : bool
If joint is True, then the noncentrality parameter for the joint
hypothesis will be returned.
If joint is True, then an array of noncentrality parameters will be
returned, where elements correspond to rows of the restriction matrix.
This correspond to the `t_test` in models and is not a quadratic form.
Returns
-------
nc : float or ndarray
Noncentrality parameter for Wald tests, correspondig to `wald_test`
or `t_test` depending on whether `joint` is true or not.
It needs to be divided by nobs to obtain effect size.
Notes
-----
Status : experimental, API will likely change
"""
if diff is None:
diff = r_matrix @ params - value # at parameter under alternative
cov_c = results.cov_params(r_matrix=r_matrix)
if joint:
nc = diff @ np.linalg.solve(cov_c, diff)
else:
nc = diff / np.sqrt(np.diag(cov_c))
return nc
def wald_test_noncent_generic(params, r_matrix, value, cov_params, diff=None,
joint=True):
"""noncentrality parameter for a wald test
The null hypothesis is ``diff = r_matrix @ params - value = 0``
Parameters
----------
params : ndarray
parameters of the model at which to evaluate noncentrality. This can
be estimated parameters or parameters under an alternative.
r_matrix : ndarray
Restriction matrix or contrasts for the Null hypothesis
value : None or ndarray
Value of the linear combination of parameters under the null
hypothesis. If value is None, then it will be replace by zero.
cov_params : ndarray
covariance matrix of the parameter estimates
diff : None or ndarray
If diff is not None, then it will be used instead of
``diff = r_matrix @ params - value``
joint : bool
If joint is True, then the noncentrality parameter for the joint
hypothesis will be returned.
If joint is True, then an array of noncentrality parameters will be
returned, where elements correspond to rows of the restriction matrix.
This correspond to the `t_test` in models and is not a quadratic form.
Returns
-------
nc : float or ndarray
Noncentrality parameter for Wald tests, correspondig to `wald_test`
or `t_test` depending on whether `joint` is true or not.
It needs to be divided by nobs to obtain effect size.
Notes
-----
Status : experimental, API will likely change
"""
if value is None:
value = 0
if diff is None:
# at parameter under alternative
diff = r_matrix @ params - value
c = r_matrix
cov_c = c.dot(cov_params).dot(c.T)
if joint:
nc = diff @ np.linalg.solve(cov_c, diff)
else:
nc = diff / np.sqrt(np.diag(cov_c))
return nc
Last update:
Nov 14, 2024