def bayes_mvs(data,alpha=0.90):
"""Return Bayesian confidence intervals for the mean, var, and std.
Assumes 1-d data all has same mean and variance and uses Jeffrey's prior
for variance and std.
alpha gives the probability that the returned interval contains
the true parameter.
Uses peak of conditional pdf as starting center.
Returns (peak, (a, b)) for each of mean, variance and standard deviation.
"""
x = ravel(data)
n = len(x)
assert(n > 1)
n = float(n)
xbar = sb.add.reduce(x)/n
C = sb.add.reduce(x*x)/n - xbar*xbar
#
fac = sqrt(C/(n-1))
tval = distributions.t.ppf((1+alpha)/2.0,n-1)
delta = fac*tval
ma = xbar - delta
mb = xbar + delta
mp = xbar
#
fac = n*C/2.0
peak = 2/(n+1.)
a = (n-1)/2.0
F_peak = distributions.invgamma.cdf(peak,a)
q1 = F_peak - alpha/2.0
q2 = F_peak + alpha/2.0
if (q1 < 0): # non-symmetric area
q2 = alpha
va = 0.0
else:
va = fac*distributions.invgamma.ppf(q1,a)
vb = fac*distributions.invgamma.ppf(q2,a)
vp = peak*fac
#
fac = sqrt(fac)
peak = sqrt(2./n)
F_peak = distributions.gengamma.cdf(peak,a,-2)
q1 = F_peak - alpha/2.0
q2 = F_peak + alpha/2.0
if (q1 < 0):
q2 = alpha
sta = 0.0
else:
sta = fac*distributions.gengamma.ppf(q1,a,-2)
stb = fac*distributions.gengamma.ppf(q2,a,-2)
stp = peak*fac
return (mp,(ma,mb)),(vp,(va,vb)),(stp,(sta,stb))
def pdf_moments(cnt):
"""Return the Gaussian expanded pdf function given the list of central
moments (first one is mean).
"""
N = len(cnt)
if N < 2:
raise ValueError, "At least two moments must be given to" + \
"approximate the pdf."
totp = poly1d(1)
sig = sqrt(cnt[1])
mu = cnt[0]
if N > 2:
Dvals = _hermnorm(N+1)
for k in range(3,N+1):
# Find Ck
Ck = 0.0
for n in range((k-3)/2):
m = k-2*n
if m % 2: # m is odd
momdiff = cnt[m-1]
else:
momdiff = cnt[m-1] - sig*sig*scipy.factorial2(m-1)
Ck += Dvals[k][m] / sig**m * momdiff
# Add to totp
totp = totp + Ck*Dvals[k]
def thisfunc(x):
xn = (x-mu)/sig
return totp(xn)*exp(-xn*xn/2.0)/sqrt(2*pi)/sig
return thisfunc
def wilcoxon(x,y=None):
"""
Calculates the Wilcoxon signed-rank test for the null hypothesis that two samples come from the same distribution. A non-parametric T-test. (need N > 20)
Returns: t-statistic, two-tailed p-value
"""
if y is None:
d = x
else:
x, y = map(asarray, (x, y))
if len(x) <> len(y):
raise ValueError, 'Unequal N in wilcoxon. Aborting.'
d = x-y
d = compress(not_equal(d,0),d) # Keep all non-zero differences
count = len(d)
if (count < 10):
print "Warning: sample size too small for normal approximation."
r = stats.rankdata(abs(d))
r_plus = sum((d > 0)*r)
r_minus = sum((d < 0)*r)
T = min(r_plus, r_minus)
mn = count*(count+1.0)*0.25
se = math.sqrt(count*(count+1)*(2*count+1.0)/24)
if (len(r) != len(unique(r))): # handle ties in data
replist, repnum = find_repeats(r)
corr = 0.0
for i in range(len(replist)):
si = repnum[i]
corr += 0.5*si*(si*si-1.0)
V = se*se - corr
se = sqrt((count*V - T*T)/(count-1.0))
z = (T - mn)/se
prob = 2*(1.0 -stats.zprob(abs(z)))
return T, prob
def mood(x,y):
"""Determine if the scale parameter for two distributions with equal
medians is the same using a Mood test.
Specifically, compute the z statistic and the probability of error
that the null hypothesis is true but rejected with the computed
statistic as the critical value.
One can reject the null hypothesis that the ratio of scale parameters is
1 if the returned probability of error is small (say < 0.05)
"""
n = len(x)
m = len(y)
xy = r_[x,y]
N = m+n
if (N < 3):
raise ValueError, "Not enough observations."
ranks = stats.rankdata(xy)
Ri = ranks[:n]
M = sum((Ri - (N+1.0)/2)**2)
# Approx stat.
mnM = n*(N*N-1.0)/12
varM = m*n*(N+1.0)*(N+2)*(N-2)/180
z = (M-mnM)/sqrt(varM)
p = distributions.norm.cdf(z)
pval = 2*min(p,1-p)
return z, pval
def circstd(samples, high=2*pi, low=0):
"""Compute the circular standard deviation for samples assumed to be in the range [low to high]
"""
ang = (samples - low)*2*pi / (high-low)
res = stats.mean(exp(1j*ang))
V = 1-abs(res)
return ((high-low)/2.0/pi) * sqrt(V)
def factorial2(n,exact=0):
"""n!! = special.gamma(n/2+1)*2**((m+1)/2)/sqrt(pi) n odd
= 2**(n) * n! n even
If exact==0, then floating point precision is used, otherwise
exact long integer is computed.
Notes:
- Array argument accepted only for exact=0 case.
- If n<0, the return value is 0.
"""
if exact:
if n < -1:
return 0L
if n <= 0:
return 1L
n = long(n)
val = 1L
k = n
while (k > 0):
val = val*k
k -= 2
return val
else:
n = asarray(n)
vals = zeros(n.shape,'d')
cond1 = (n % 2) & (n >= -1)
cond2 = (1-(n % 2)) & (n >= -1)
oddn = extract(cond1,n)
evenn = extract(cond2,n)
nd2o = oddn / 2.0
nd2e = evenn / 2.0
insert(vals,cond1,special.gamma(nd2o+1)/sqrt(pi)*pow(2.0,nd2o+0.5))
insert(vals,cond2,special.gamma(nd2e+1) * pow(2.0,nd2e))
return vals
def norm(x, ord=2):
""" norm(x, ord=2) -> n
Matrix and vector norm.
Inputs:
x -- a rank-1 (vector) or rank-2 (matrix) array
ord -- the order of norm.
Comments:
For vectors ord can be any real number including Inf or -Inf.
ord = Inf, computes the maximum of the magnitudes
ord = -Inf, computes minimum of the magnitudes
ord is finite, computes sum(abs(x)**ord)**(1.0/ord)
For matrices ord can only be + or - 1, 2, Inf.
ord = 2 computes the largest singular value
ord = -2 computes the smallest singular value
ord = 1 computes the largest column sum of absolute values
ord = -1 computes the smallest column sum of absolute values
ord = Inf computes the largest row sum of absolute values
ord = -Inf computes the smallest row sum of absolute values
ord = 'fro' computes the frobenius norm sqrt(sum(diag(X.H * X)))
"""
x = asarray_chkfinite(x)
nd = len(x.shape)
Inf = scipy_base.Inf
if nd == 1:
if ord == Inf:
return scipy_base.amax(abs(x))
elif ord == -Inf:
return scipy_base.amin(abs(x))
else:
return scipy_base.sum(abs(x)**ord)**(1.0/ord)
elif nd == 2:
if ord == 2:
return scipy_base.amax(decomp.svd(x,compute_uv=0))
elif ord == -2:
return scipy_base.amin(decomp.svd(x,compute_uv=0))
elif ord == 1:
return scipy_base.amax(scipy_base.sum(abs(x)))
elif ord == Inf:
return scipy_base.amax(scipy_base.sum(abs(x),axis=1))
elif ord == -1:
return scipy_base.amin(scipy_base.sum(abs(x)))
elif ord == -Inf:
return scipy_base.amin(scipy_base.sum(abs(x),axis=1))
elif ord in ['fro','f']:
val = real((conjugate(x)*x).flat)
return sqrt(add.reduce(val))
else:
raise ValueError, "Invalid norm order for matrices."
else:
raise ValueError, "Improper number of dimensions to norm."
def pdf_fromgamma(g1,g2,g3=0.0,g4=None):
if g4 is None:
g4 = 3*g2*g2
sigsq = 1.0/g2
sig = sqrt(sigsq)
mu = g1*sig**3.0
p12 = _hermnorm(13)
for k in range(13):
p12[k] = p12[k]/sig**k
# Add all of the terms to polynomial
totp = p12[0] - (g1/6.0*p12[3]) + \
(g2/24.0*p12[4] +g1*g1/72.0*p12[6]) - \
(g3/120.0*p12[5] + g1*g2/144.0*p12[7] + g1**3.0/1296.0*p12[9]) + \
(g4/720*p12[6] + (g2*g2/1152.0+g1*g3/720)*p12[8] +
g1*g1*g2/1728.0*p12[10] + g1**4.0/31104.0*p12[12])
# Final normalization
totp = totp / sqrt(2*pi)/sig
def thefunc(x):
xn = (x-mu)/sig
return totp(xn)*exp(-xn*xn/2.0)
return thefunc
def thisfunc(x):
xn = (x-mu)/sig
return totp(xn)*exp(-xn*xn/2.0)/sqrt(2*pi)/sig
def ansari(x,y):
"""Determine if the scale parameter for two distributions with equal
medians is the same using the Ansari-Bradley statistic.
Specifically, compute the AB statistic and the probability of error
that the null hypothesis is true but rejected with the computed
statistic as the critical value.
One can reject the null hypothesis that the ratio of variances is 1 if
returned probability of error is small (say < 0.05)
"""
x,y = asarray(x),asarray(y)
n = len(x)
m = len(y)
if (m < 1):
raise ValueError, "Not enough other observations."
if (n < 1):
raise ValueError, "Not enough test observations."
N = m+n
xy = r_[x,y] # combine
rank = stats.rankdata(xy)
symrank = amin(array((rank,N-rank+1)),0)
AB = sum(symrank[:n])
uxy = unique(xy)
repeats = (len(uxy) != len(xy))
exact = ((m<55) and (n<55) and not repeats)
if repeats and ((m < 55) or (n < 55)):
print "Ties preclude use of exact statistic."
if exact:
astart, a1, ifault = statlib.gscale(n,m)
ind = AB-astart
total = sum(a1)
if ind < len(a1)/2.0:
cind = int(ceil(ind))
if (ind == cind):
pval = 2.0*sum(a1[:cind+1])/total
else:
pval = 2.0*sum(a1[:cind])/total
else:
find = int(floor(ind))
if (ind == floor(ind)):
pval = 2.0*sum(a1[find:])/total
else:
pval = 2.0*sum(a1[find+1:])/total
return AB, min(1.0,pval)
# otherwise compute normal approximation
if N % 2: # N odd
mnAB = n*(N+1.0)**2 / 4.0 / N
varAB = n*m*(N+1.0)*(3+N**2)/(48.0*N**2)
else:
mnAB = n*(N+2.0)/4.0
varAB = m*n*(N+2)*(N-2.0)/48/(N-1.0)
if repeats: # adjust variance estimates
# compute sum(tj * rj**2)
fac = sum(symrank**2)
if N % 2: # N odd
varAB = m*n*(16*N*fac-(N+1)**4)/(16.0 * N**2 * (N-1))
else: # N even
varAB = m*n*(16*fac-N*(N+2)**2)/(16.0 * N * (N-1))
z = (AB - mnAB)/sqrt(varAB)
pval = (1-distributions.norm.cdf(abs(z)))*2.0
return AB, pval
def anderson(x,dist='norm'):
"""Anderson and Darling test for normal, exponential, or Gumbel
(Extreme Value Type I) distribution.
Given samples x, return A2, the Anderson-Darling statistic,
the significance levels in percentages, and the corresponding
critical values.
Critical values provided are for the following significance levels
norm/expon: 15%, 10%, 5%, 2.5%, 1%
Gumbel: 25%, 10%, 5%, 2.5%, 1%
logistic: 25%, 10%, 5%, 2.5%, 1%, 0.5%
If A2 is larger than these critical values then for that significance
level, the hypothesis that the data come from a normal (exponential)
can be rejected.
"""
if not dist in ['norm','expon','gumbel','extreme1','logistic']:
raise ValueError, "Invalid distribution."
y = sort(x)
xbar = stats.mean(x)
N = len(y)
if dist == 'norm':
s = stats.std(x)
w = (y-xbar)/s
z = distributions.norm.cdf(w)
sig = array([15,10,5,2.5,1])
critical = around(_Avals_norm / (1.0 + 4.0/N - 25.0/N/N),3)
elif dist == 'expon':
w = y / xbar
z = distributions.expon.cdf(w)
sig = array([15,10,5,2.5,1])
critical = around(_Avals_expon / (1.0 + 0.6/N),3)
elif dist == 'logistic':
def rootfunc(ab,xj,N):
a,b = ab
tmp = (xj-a)/b
tmp2 = exp(tmp)
val = [sum(1.0/(1+tmp2))-0.5*N,
sum(tmp*(1.0-tmp2)/(1+tmp2))+N]
return array(val)
sol0=array([xbar,stats.std(x)])
sol = optimize.fsolve(rootfunc,sol0,args=(x,N),xtol=1e-5)
w = (y-sol[0])/sol[1]
z = distributions.logistic.cdf(w)
sig = array([25,10,5,2.5,1,0.5])
critical = around(_Avals_logistic / (1.0+0.25/N),3)
else:
def fixedsolve(th,xj,N):
val = stats.sum(xj)*1.0/N
tmp = exp(-xj/th)
term = sum(xj*tmp)
term /= sum(tmp)
return val - term
s = optimize.fixed_point(fixedsolve, 1.0, args=(x,N),xtol=1e-5)
xbar = -s*log(sum(exp(-x/s))*1.0/N)
w = (y-xbar)/s
z = distributions.gumbel_l.cdf(w)
sig = array([25,10,5,2.5,1])
critical = around(_Avals_gumbel / (1.0 + 0.2/sqrt(N)),3)
i = arange(1,N+1)
S = sum((2*i-1.0)/N*(log(z)+log(1-z[::-1])))
A2 = -N-S
return A2, critical, sig
