def shapiro(x,a=None,reta=False):
"""
Perform the Shapiro-Wilk test for normality.
The Shapiro-Wilk test tests the null hypothesis that the
data was drawn from a normal distribution.
Parameters
----------
x : array_like
Array of sample data.
a : array_like, optional
Array of internal parameters used in the calculation. If these
are not given, they will be computed internally. If x has length
n, then a must have length n/2.
reta : bool, optional
Whether or not to return the internally computed a values. The
default is False.
Returns
-------
W : float
The test statistic.
p-value : float
The p-value for the hypothesis test.
a : array_like, optional
If `reta` is True, then these are the internally computed "a"
values that may be passed into this function on future calls.
See Also
--------
anderson : The Anderson-Darling test for normality
References
----------
.. [1] http://www.itl.nist.gov/div898/handbook/prc/section2/prc213.htm
"""
N = len(x)
if N < 3:
raise ValueError("Data must be at least length 3.")
if a is None:
a = zeros(N,'f')
init = 0
else:
if len(a) != N//2:
raise ValueError("len(a) must equal len(x)/2")
init = 1
y = sort(x)
a, w, pw, ifault = statlib.swilk(y, a[:N//2], init)
if not ifault in [0,2]:
warnings.warn(str(ifault))
if N > 5000:
warnings.warn("p-value may not be accurate for N > 5000.")
if reta:
return w, pw, a
else:
return w, pw
def shapiro(x,a=None,reta=0):
"""Shapiro and Wilk test for normality.
Given random variates x, compute the W statistic and its p-value
for a normality test.
If p-value is high, one cannot reject the null hypothesis of normality
with this test. P-value is probability that the W statistic is
as low as it is if the samples are actually from a normal distribution.
Output: W statistic and its p-value
if reta is nonzero then also return the computed "a" values
as the third output. If these are known for a given size
they can be given as input instead of computed internally.
"""
N = len(x)
if N < 3:
raise ValueError, "Data must be at least length 3."
if a is None:
a = zeros(N,'f')
init = 0
else:
assert(len(a) == N/2), "a must be == len(x)/2"
init = 1
y = sort(x)
a,w,pw,ifault = statlib.swilk(y,a[:N/2],init)
if not ifault in [0,2]:
print ifault
if N > 5000:
print "p-value may not be accurate for N > 5000."
if reta:
return w, pw, a
else:
return w, pw
def shapiro(x,a=None,reta=0):
"""Shapiro and Wilk test for normality.
Given random variates x, compute the W statistic and its p-value
for a normality test.
If p-value is high, one cannot reject the null hypothesis of normality
with this test. P-value is probability that the W statistic is
as low as it is if the samples are actually from a normal distribution.
Output: W statistic and its p-value
if reta is nonzero then also return the computed "a" values
as the third output. If these are known for a given size
they can be given as input instead of computed internally.
"""
N = len(x)
if N < 3:
raise ValueError, "Data must be at least length 3."
if a is None:
a = zeros(N,'f')
init = 0
else:
assert(len(a) == N/2), "a must be == len(x)/2"
init = 1
y = sort(x)
a,w,pw,ifault = statlib.swilk(y,a[:N/2],init)
if not ifault in [0,2]:
warnings.warn(str(ifault))
if N > 5000:
warnings.warn("p-value may not be accurate for N > 5000.")
if reta:
return w, pw, a
else:
return w, pw
