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Fixed database typo and removed unnecessary class identifier.

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Batuhan Berk Başoğlu 2020-10-14 10:10:37 -04:00
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"""
Additional statistics functions with support for masked arrays.
"""
# Original author (2007): Pierre GF Gerard-Marchant
__all__ = ['compare_medians_ms',
'hdquantiles', 'hdmedian', 'hdquantiles_sd',
'idealfourths',
'median_cihs','mjci','mquantiles_cimj',
'rsh',
'trimmed_mean_ci',]
import numpy as np
from numpy import float_, int_, ndarray
import numpy.ma as ma
from numpy.ma import MaskedArray
from . import mstats_basic as mstats
from scipy.stats.distributions import norm, beta, t, binom
def hdquantiles(data, prob=list([.25,.5,.75]), axis=None, var=False,):
"""
Computes quantile estimates with the Harrell-Davis method.
The quantile estimates are calculated as a weighted linear combination
of order statistics.
Parameters
----------
data : array_like
Data array.
prob : sequence, optional
Sequence of quantiles to compute.
axis : int or None, optional
Axis along which to compute the quantiles. If None, use a flattened
array.
var : bool, optional
Whether to return the variance of the estimate.
Returns
-------
hdquantiles : MaskedArray
A (p,) array of quantiles (if `var` is False), or a (2,p) array of
quantiles and variances (if `var` is True), where ``p`` is the
number of quantiles.
See Also
--------
hdquantiles_sd
"""
def _hd_1D(data,prob,var):
"Computes the HD quantiles for a 1D array. Returns nan for invalid data."
xsorted = np.squeeze(np.sort(data.compressed().view(ndarray)))
# Don't use length here, in case we have a numpy scalar
n = xsorted.size
hd = np.empty((2,len(prob)), float_)
if n < 2:
hd.flat = np.nan
if var:
return hd
return hd[0]
v = np.arange(n+1) / float(n)
betacdf = beta.cdf
for (i,p) in enumerate(prob):
_w = betacdf(v, (n+1)*p, (n+1)*(1-p))
w = _w[1:] - _w[:-1]
hd_mean = np.dot(w, xsorted)
hd[0,i] = hd_mean
#
hd[1,i] = np.dot(w, (xsorted-hd_mean)**2)
#
hd[0, prob == 0] = xsorted[0]
hd[0, prob == 1] = xsorted[-1]
if var:
hd[1, prob == 0] = hd[1, prob == 1] = np.nan
return hd
return hd[0]
# Initialization & checks
data = ma.array(data, copy=False, dtype=float_)
p = np.array(prob, copy=False, ndmin=1)
# Computes quantiles along axis (or globally)
if (axis is None) or (data.ndim == 1):
result = _hd_1D(data, p, var)
else:
if data.ndim > 2:
raise ValueError("Array 'data' must be at most two dimensional, "
"but got data.ndim = %d" % data.ndim)
result = ma.apply_along_axis(_hd_1D, axis, data, p, var)
return ma.fix_invalid(result, copy=False)
def hdmedian(data, axis=-1, var=False):
"""
Returns the Harrell-Davis estimate of the median along the given axis.
Parameters
----------
data : ndarray
Data array.
axis : int, optional
Axis along which to compute the quantiles. If None, use a flattened
array.
var : bool, optional
Whether to return the variance of the estimate.
Returns
-------
hdmedian : MaskedArray
The median values. If ``var=True``, the variance is returned inside
the masked array. E.g. for a 1-D array the shape change from (1,) to
(2,).
"""
result = hdquantiles(data,[0.5], axis=axis, var=var)
return result.squeeze()
def hdquantiles_sd(data, prob=list([.25,.5,.75]), axis=None):
"""
The standard error of the Harrell-Davis quantile estimates by jackknife.
Parameters
----------
data : array_like
Data array.
prob : sequence, optional
Sequence of quantiles to compute.
axis : int, optional
Axis along which to compute the quantiles. If None, use a flattened
array.
Returns
-------
hdquantiles_sd : MaskedArray
Standard error of the Harrell-Davis quantile estimates.
See Also
--------
hdquantiles
"""
def _hdsd_1D(data, prob):
"Computes the std error for 1D arrays."
xsorted = np.sort(data.compressed())
n = len(xsorted)
hdsd = np.empty(len(prob), float_)
if n < 2:
hdsd.flat = np.nan
vv = np.arange(n) / float(n-1)
betacdf = beta.cdf
for (i,p) in enumerate(prob):
_w = betacdf(vv, (n+1)*p, (n+1)*(1-p))
w = _w[1:] - _w[:-1]
mx_ = np.fromiter([np.dot(w,xsorted[np.r_[list(range(0,k)),
list(range(k+1,n))].astype(int_)])
for k in range(n)], dtype=float_)
mx_var = np.array(mx_.var(), copy=False, ndmin=1) * n / float(n-1)
hdsd[i] = float(n-1) * np.sqrt(np.diag(mx_var).diagonal() / float(n))
return hdsd
# Initialization & checks
data = ma.array(data, copy=False, dtype=float_)
p = np.array(prob, copy=False, ndmin=1)
# Computes quantiles along axis (or globally)
if (axis is None):
result = _hdsd_1D(data, p)
else:
if data.ndim > 2:
raise ValueError("Array 'data' must be at most two dimensional, "
"but got data.ndim = %d" % data.ndim)
result = ma.apply_along_axis(_hdsd_1D, axis, data, p)
return ma.fix_invalid(result, copy=False).ravel()
def trimmed_mean_ci(data, limits=(0.2,0.2), inclusive=(True,True),
alpha=0.05, axis=None):
"""
Selected confidence interval of the trimmed mean along the given axis.
Parameters
----------
data : array_like
Input data.
limits : {None, tuple}, optional
None or a two item tuple.
Tuple of the percentages to cut on each side of the array, with respect
to the number of unmasked data, as floats between 0. and 1. If ``n``
is the number of unmasked data before trimming, then
(``n * limits[0]``)th smallest data and (``n * limits[1]``)th
largest data are masked. The total number of unmasked data after
trimming is ``n * (1. - sum(limits))``.
The value of one limit can be set to None to indicate an open interval.
Defaults to (0.2, 0.2).
inclusive : (2,) tuple of boolean, optional
If relative==False, tuple indicating whether values exactly equal to
the absolute limits are allowed.
If relative==True, tuple indicating whether the number of data being
masked on each side should be rounded (True) or truncated (False).
Defaults to (True, True).
alpha : float, optional
Confidence level of the intervals.
Defaults to 0.05.
axis : int, optional
Axis along which to cut. If None, uses a flattened version of `data`.
Defaults to None.
Returns
-------
trimmed_mean_ci : (2,) ndarray
The lower and upper confidence intervals of the trimmed data.
"""
data = ma.array(data, copy=False)
trimmed = mstats.trimr(data, limits=limits, inclusive=inclusive, axis=axis)
tmean = trimmed.mean(axis)
tstde = mstats.trimmed_stde(data,limits=limits,inclusive=inclusive,axis=axis)
df = trimmed.count(axis) - 1
tppf = t.ppf(1-alpha/2.,df)
return np.array((tmean - tppf*tstde, tmean+tppf*tstde))
def mjci(data, prob=[0.25,0.5,0.75], axis=None):
"""
Returns the Maritz-Jarrett estimators of the standard error of selected
experimental quantiles of the data.
Parameters
----------
data : ndarray
Data array.
prob : sequence, optional
Sequence of quantiles to compute.
axis : int or None, optional
Axis along which to compute the quantiles. If None, use a flattened
array.
"""
def _mjci_1D(data, p):
data = np.sort(data.compressed())
n = data.size
prob = (np.array(p) * n + 0.5).astype(int_)
betacdf = beta.cdf
mj = np.empty(len(prob), float_)
x = np.arange(1,n+1, dtype=float_) / n
y = x - 1./n
for (i,m) in enumerate(prob):
W = betacdf(x,m-1,n-m) - betacdf(y,m-1,n-m)
C1 = np.dot(W,data)
C2 = np.dot(W,data**2)
mj[i] = np.sqrt(C2 - C1**2)
return mj
data = ma.array(data, copy=False)
if data.ndim > 2:
raise ValueError("Array 'data' must be at most two dimensional, "
"but got data.ndim = %d" % data.ndim)
p = np.array(prob, copy=False, ndmin=1)
# Computes quantiles along axis (or globally)
if (axis is None):
return _mjci_1D(data, p)
else:
return ma.apply_along_axis(_mjci_1D, axis, data, p)
def mquantiles_cimj(data, prob=[0.25,0.50,0.75], alpha=0.05, axis=None):
"""
Computes the alpha confidence interval for the selected quantiles of the
data, with Maritz-Jarrett estimators.
Parameters
----------
data : ndarray
Data array.
prob : sequence, optional
Sequence of quantiles to compute.
alpha : float, optional
Confidence level of the intervals.
axis : int or None, optional
Axis along which to compute the quantiles.
If None, use a flattened array.
Returns
-------
ci_lower : ndarray
The lower boundaries of the confidence interval. Of the same length as
`prob`.
ci_upper : ndarray
The upper boundaries of the confidence interval. Of the same length as
`prob`.
"""
alpha = min(alpha, 1 - alpha)
z = norm.ppf(1 - alpha/2.)
xq = mstats.mquantiles(data, prob, alphap=0, betap=0, axis=axis)
smj = mjci(data, prob, axis=axis)
return (xq - z * smj, xq + z * smj)
def median_cihs(data, alpha=0.05, axis=None):
"""
Computes the alpha-level confidence interval for the median of the data.
Uses the Hettmasperger-Sheather method.
Parameters
----------
data : array_like
Input data. Masked values are discarded. The input should be 1D only,
or `axis` should be set to None.
alpha : float, optional
Confidence level of the intervals.
axis : int or None, optional
Axis along which to compute the quantiles. If None, use a flattened
array.
Returns
-------
median_cihs
Alpha level confidence interval.
"""
def _cihs_1D(data, alpha):
data = np.sort(data.compressed())
n = len(data)
alpha = min(alpha, 1-alpha)
k = int(binom._ppf(alpha/2., n, 0.5))
gk = binom.cdf(n-k,n,0.5) - binom.cdf(k-1,n,0.5)
if gk < 1-alpha:
k -= 1
gk = binom.cdf(n-k,n,0.5) - binom.cdf(k-1,n,0.5)
gkk = binom.cdf(n-k-1,n,0.5) - binom.cdf(k,n,0.5)
I = (gk - 1 + alpha)/(gk - gkk)
lambd = (n-k) * I / float(k + (n-2*k)*I)
lims = (lambd*data[k] + (1-lambd)*data[k-1],
lambd*data[n-k-1] + (1-lambd)*data[n-k])
return lims
data = ma.array(data, copy=False)
# Computes quantiles along axis (or globally)
if (axis is None):
result = _cihs_1D(data, alpha)
else:
if data.ndim > 2:
raise ValueError("Array 'data' must be at most two dimensional, "
"but got data.ndim = %d" % data.ndim)
result = ma.apply_along_axis(_cihs_1D, axis, data, alpha)
return result
def compare_medians_ms(group_1, group_2, axis=None):
"""
Compares the medians from two independent groups along the given axis.
The comparison is performed using the McKean-Schrader estimate of the
standard error of the medians.
Parameters
----------
group_1 : array_like
First dataset. Has to be of size >=7.
group_2 : array_like
Second dataset. Has to be of size >=7.
axis : int, optional
Axis along which the medians are estimated. If None, the arrays are
flattened. If `axis` is not None, then `group_1` and `group_2`
should have the same shape.
Returns
-------
compare_medians_ms : {float, ndarray}
If `axis` is None, then returns a float, otherwise returns a 1-D
ndarray of floats with a length equal to the length of `group_1`
along `axis`.
"""
(med_1, med_2) = (ma.median(group_1,axis=axis), ma.median(group_2,axis=axis))
(std_1, std_2) = (mstats.stde_median(group_1, axis=axis),
mstats.stde_median(group_2, axis=axis))
W = np.abs(med_1 - med_2) / ma.sqrt(std_1**2 + std_2**2)
return 1 - norm.cdf(W)
def idealfourths(data, axis=None):
"""
Returns an estimate of the lower and upper quartiles.
Uses the ideal fourths algorithm.
Parameters
----------
data : array_like
Input array.
axis : int, optional
Axis along which the quartiles are estimated. If None, the arrays are
flattened.
Returns
-------
idealfourths : {list of floats, masked array}
Returns the two internal values that divide `data` into four parts
using the ideal fourths algorithm either along the flattened array
(if `axis` is None) or along `axis` of `data`.
"""
def _idf(data):
x = data.compressed()
n = len(x)
if n < 3:
return [np.nan,np.nan]
(j,h) = divmod(n/4. + 5/12.,1)
j = int(j)
qlo = (1-h)*x[j-1] + h*x[j]
k = n - j
qup = (1-h)*x[k] + h*x[k-1]
return [qlo, qup]
data = ma.sort(data, axis=axis).view(MaskedArray)
if (axis is None):
return _idf(data)
else:
return ma.apply_along_axis(_idf, axis, data)
def rsh(data, points=None):
"""
Evaluates Rosenblatt's shifted histogram estimators for each data point.
Rosenblatt's estimator is a centered finite-difference approximation to the
derivative of the empirical cumulative distribution function.
Parameters
----------
data : sequence
Input data, should be 1-D. Masked values are ignored.
points : sequence or None, optional
Sequence of points where to evaluate Rosenblatt shifted histogram.
If None, use the data.
"""
data = ma.array(data, copy=False)
if points is None:
points = data
else:
points = np.array(points, copy=False, ndmin=1)
if data.ndim != 1:
raise AttributeError("The input array should be 1D only !")
n = data.count()
r = idealfourths(data, axis=None)
h = 1.2 * (r[-1]-r[0]) / n**(1./5)
nhi = (data[:,None] <= points[None,:] + h).sum(0)
nlo = (data[:,None] < points[None,:] - h).sum(0)
return (nhi-nlo) / (2.*n*h)