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DOC: numpydoc validation of morestats.py (scipy#11017)
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* Resolve numpydoc SS03 error

* Resolve numpydoc PR08 error

* Resolve numpydoc PR09 error

* Resolve numpydoc RT05 error

* Resolve numpydoc GL06 error

* Resolve numpydoc GL07 error

* Resolve numpydoc PR03 error

* Resolve numpydoc PR05 error

* Resolve numpydoc PR06 error

* Resolve numpydoc PR09 error

* Resolve numpydoc PR10 error

* Resolve numpydoc SS05 error

* Resolve numpydoc SS06 error

* Resolve Travis error in stats.wilcoxon

* Resolve Travis error in stats.wilcoxon

* Single backticks -->> double backticks
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@brandondavid @rgommers
brandondavid authored and rgommers committed Nov 8, 2019
1 parent 1567615 commit 89286b9
Showing 1 changed file with 48 additions and 46 deletions.
94 changes: 48 additions & 46 deletions scipy/stats/morestats.py
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Original file line number Diff line number Diff line change
Expand Up @@ -149,11 +149,11 @@ def mvsdist(data):
Returns
-------
mdist : "frozen" distribution object
Distribution object representing the mean of the data
Distribution object representing the mean of the data.
vdist : "frozen" distribution object
Distribution object representing the variance of the data
Distribution object representing the variance of the data.
sdist : "frozen" distribution object
Distribution object representing the standard deviation of the data
Distribution object representing the standard deviation of the data.
See Also
--------
Expand Down Expand Up @@ -308,7 +308,7 @@ def kstat(data, n=2):

def kstatvar(data, n=2):
r"""
Returns an unbiased estimator of the variance of the k-statistic.
Return an unbiased estimator of the variance of the k-statistic.
See `kstat` for more details of the k-statistic.
Expand Down Expand Up @@ -629,7 +629,7 @@ def probplot(x, sparams=(), dist='norm', fit=True, plot=None, rvalue=False):

def ppcc_max(x, brack=(0.0, 1.0), dist='tukeylambda'):
"""
Calculate the shape parameter that maximizes the PPCC
Calculate the shape parameter that maximizes the PPCC.
The probability plot correlation coefficient (PPCC) plot can be used to
determine the optimal shape parameter for a one-parameter family of
Expand All @@ -656,7 +656,7 @@ def ppcc_max(x, brack=(0.0, 1.0), dist='tukeylambda'):
The shape parameter at which the probability plot correlation
coefficient reaches its max value.
See also
See Also
--------
ppcc_plot, probplot, boxcox
Expand Down Expand Up @@ -736,7 +736,7 @@ def ppcc_plot(x, a, b, dist='tukeylambda', plot=None, N=80):
----------
x : array_like
Input array.
a, b: scalar
a, b : scalar
Lower and upper bounds of the shape parameter to use.
dist : str or stats.distributions instance, optional
Distribution or distribution function name. Objects that look enough
Expand All @@ -759,7 +759,7 @@ def ppcc_plot(x, a, b, dist='tukeylambda', plot=None, N=80):
ppcc : ndarray
The calculated probability plot correlation coefficient values.
See also
See Also
--------
ppcc_max, probplot, boxcox_normplot, tukeylambda
Expand Down Expand Up @@ -1469,7 +1469,10 @@ def yeojohnson_llf(lmb, data):


def yeojohnson_normmax(x, brack=(-2, 2)):
"""Compute optimal Yeo-Johnson transform parameter for input data, using
"""
Compute optimal Yeo-Johnson transform parameter.
Compute optimal Yeo-Johnson transform parameter for input data, using
maximum likelihood estimation.
Parameters
Expand All @@ -1486,14 +1489,14 @@ def yeojohnson_normmax(x, brack=(-2, 2)):
maxlog : float
The optimal transform parameter found.
Notes
-----
.. versionadded:: 1.2.0
See Also
--------
yeojohnson, yeojohnson_llf, yeojohnson_normplot
Notes
-----
.. versionadded:: 1.2.0
Examples
--------
>>> from scipy import stats
Expand Down Expand Up @@ -1683,7 +1686,7 @@ def shapiro(x):

def anderson(x, dist='norm'):
"""
Anderson-Darling test for data coming from a particular distribution
Anderson-Darling test for data coming from a particular distribution.
The Anderson-Darling tests the null hypothesis that a sample is
drawn from a population that follows a particular distribution.
Expand All @@ -1695,7 +1698,7 @@ def anderson(x, dist='norm'):
Parameters
----------
x : array_like
array of sample data
Array of sample data.
dist : {'norm','expon','logistic','gumbel','gumbel_l', gumbel_r',
'extreme1'}, optional
the type of distribution to test against. The default is 'norm'
Expand All @@ -1704,9 +1707,9 @@ def anderson(x, dist='norm'):
Returns
-------
statistic : float
The Anderson-Darling test statistic
The Anderson-Darling test statistic.
critical_values : list
The critical values for this distribution
The critical values for this distribution.
significance_level : list
The significance levels for the corresponding critical values
in percents. The function returns critical values for a
Expand Down Expand Up @@ -2059,7 +2062,7 @@ def anderson_ksamp(samples, midrank=True):

def ansari(x, y):
"""
Perform the Ansari-Bradley test for equal scale parameters
Perform the Ansari-Bradley test for equal scale parameters.
The Ansari-Bradley test is a non-parametric test for the equality
of the scale parameter of the distributions from which two
Expand All @@ -2068,14 +2071,14 @@ def ansari(x, y):
Parameters
----------
x, y : array_like
arrays of sample data
Arrays of sample data.
Returns
-------
statistic : float
The Ansari-Bradley test statistic
The Ansari-Bradley test statistic.
pvalue : float
The p-value of the hypothesis test
The p-value of the hypothesis test.
See Also
--------
Expand Down Expand Up @@ -2155,7 +2158,7 @@ def ansari(x, y):

def bartlett(*args):
"""
Perform Bartlett's test for equal variances
Perform Bartlett's test for equal variances.
Bartlett's test tests the null hypothesis that all input samples
are from populations with equal variances. For samples
Expand Down Expand Up @@ -2365,23 +2368,23 @@ def binom_test(x, n=None, p=0.5, alternative='two-sided'):
Parameters
----------
x : integer or array_like
the number of successes, or if x has length 2, it is the
x : int or array_like
The number of successes, or if x has length 2, it is the
number of successes and the number of failures.
n : integer
the number of trials. This is ignored if x gives both the
number of successes and failures
n : int
The number of trials. This is ignored if x gives both the
number of successes and failures.
p : float, optional
The hypothesized probability of success. 0 <= p <= 1. The
default value is p = 0.5
The hypothesized probability of success. ``0 <= p <= 1``. The
default value is ``p = 0.5``.
alternative : {'two-sided', 'greater', 'less'}, optional
Indicates the alternative hypothesis. The default value is
'two-sided'.
Returns
-------
p-value : float
The p-value of the hypothesis test
The p-value of the hypothesis test.
References
----------
Expand Down Expand Up @@ -2733,15 +2736,14 @@ def wilcoxon(x, y=None, zero_method="wilcox", correction=False,
Either the second set of measurements (if `x` is the first set of
measurements), or not specified (if `x` is the differences between
two sets of measurements.) Must be one-dimensional.
zero_method : {"pratt", "wilcox", "zsplit"}, optional. Default is "wilcox".
"pratt":
includes zero-differences in the ranking process,
but drops the ranks of the zeros, see [4]_, (more conservative)
"wilcox":
discards all zero-differences, the default
"zsplit":
includes zero-differences in the ranking process and split the
zero rank between positive and negative ones
zero_method : {'pratt', 'wilcox', 'zsplit'}, optional
The following options are available (default is 'wilcox'):
* 'pratt': Includes zero-differences in the ranking process,
but drops the ranks of the zeros, see [4]_, (more conservative).
* 'wilcox': Discards all zero-differences, the default.
* 'zsplit': Includes zero-differences in the ranking process and
split the zero rank between positive and negative ones.
correction : bool, optional
If True, apply continuity correction by adjusting the Wilcoxon rank
statistic by 0.5 towards the mean value when computing the
Expand Down Expand Up @@ -2925,7 +2927,7 @@ def wilcoxon(x, y=None, zero_method="wilcox", correction=False,

def median_test(*args, **kwds):
"""
Mood's median test.
Perform a Mood's median test.
Test that two or more samples come from populations with the same median.
Expand Down Expand Up @@ -2958,7 +2960,7 @@ def median_test(*args, **kwds):
If True, *and* there are just two samples, apply Yates' correction
for continuity when computing the test statistic associated with
the contingency table. Default is True.
lambda_ : float or str, optional.
lambda_ : float or str, optional
By default, the statistic computed in this test is Pearson's
chi-squared statistic. `lambda_` allows a statistic from the
Cressie-Read power divergence family to be used instead. See
Expand Down Expand Up @@ -3242,16 +3244,16 @@ def circmean(samples, high=2*pi, low=0, axis=None, nan_policy='propagate'):

def circvar(samples, high=2*pi, low=0, axis=None, nan_policy='propagate'):
"""
Compute the circular variance for samples assumed to be in a range
Compute the circular variance for samples assumed to be in a range.
Parameters
----------
samples : array_like
Input array.
low : float or int, optional
Low boundary for circular variance range. Default is 0.
high : float or int, optional
High boundary for circular variance range. Default is ``2*pi``.
low : float or int, optional
Low boundary for circular variance range. Default is 0.
axis : int, optional
Axis along which variances are computed. The default is to compute
the variance of the flattened array.
Expand Down Expand Up @@ -3301,11 +3303,11 @@ def circstd(samples, high=2*pi, low=0, axis=None, nan_policy='propagate'):
----------
samples : array_like
Input array.
low : float or int, optional
Low boundary for circular standard deviation range. Default is 0.
high : float or int, optional
High boundary for circular standard deviation range.
Default is ``2*pi``.
low : float or int, optional
Low boundary for circular standard deviation range. Default is 0.
axis : int, optional
Axis along which standard deviations are computed. The default is
to compute the standard deviation of the flattened array.
Expand Down

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