def chi_square(x, p, df=1):
"""returns the chisquare statistic and it's probability"""
N = len(x)
end = N
sim = numpy.logical_not(numpy.logical_xor(x[0:end-p], x[p:end]))*1
s = ((numpy.ones((N-p,), float)-sim)**2).sum()
D = s/(N-p)
p_val = 1 - igam(df/2.0, D/2)
return D, p_val
def chi_square(x, p, df=1):
"""returns the chisquare statistic and it's probability"""
N = len(x)
end = N
sim = numpy.logical_not(numpy.logical_xor(x[0:end - p], x[p:end])) * 1
s = ((numpy.ones((N - p, ), float) - sim)**2).sum()
D = s / (N - p)
p_val = 1 - igam(df / 2.0, D / 2)
return D, p_val
def pdtrc(k, m):
"""Returns sum of right tail of Poisson distribution, k+1 through infinity.
See Cephes docs for details.
"""
if k < 0:
raise ValueError, "Poisson k must be >= 0."
if m < 0:
raise ValueError, "Poisson m must be >= 0."
return igam(k+1, m)
def pdtrc(k, m):
"""Returns sum of right tail of Poisson distribution, k+1 through infinity.
See Cephes docs for details.
"""
if k < 0:
raise ValueError, "Poisson k must be >= 0."
if m < 0:
raise ValueError, "Poisson m must be >= 0."
return igam(k + 1, m)
def chi_low(x, df):
"""Returns left-hand tail of chi-square distribution (0 to x), given df.
x ranges from 0 to infinity.
df, the degrees of freedom, ranges from 1 to infinity (assume integers).
Typically, df is (r-1)*(c-1) for a r by c table.
Result ranges from 0 to 1.
See Cephes docs for details.
"""
x = fix_rounding_error(x)
if x < 0:
raise ValueError, "chi_low: x must be >= 0 (got %s)." % x
if df < 1:
raise ValueError, "chi_low: df must be >= 1 (got %s)." % df
return igam(df/2, x/2)
def chi_low(x, df):
"""Returns left-hand tail of chi-square distribution (0 to x), given df.
x ranges from 0 to infinity.
df, the degrees of freedom, ranges from 1 to infinity (assume integers).
Typically, df is (r-1)*(c-1) for a r by c table.
Result ranges from 0 to 1.
See Cephes docs for details.
"""
x = fix_rounding_error(x)
if x < 0:
raise ValueError, "chi_low: x must be >= 0 (got %s)." % x
if df < 1:
raise ValueError, "chi_low: df must be >= 1 (got %s)." % df
return igam(df / 2, x / 2)
def gdtr(a, b, x):
"""Returns integral from 0 to x of Gamma distribution with params a and b.
"""
if x < 0.0:
raise ZeroDivisionError, "x must be at least 0."
return igam( b, a * x)
def gdtr(a, b, x):
"""Returns integral from 0 to x of Gamma distribution with params a and b.
"""
if x < 0.0:
raise ZeroDivisionError, "x must be at least 0."
return igam(b, a * x)
