def tailAreaProbability(self, theStat, verbose=1):
"""Access to :func:`func.tailAreaProbability`
Supply *theStat*, ie the test quantity, and it uses
``self.data`` to make the distribution.
"""
return func.tailAreaProbability(theStat, self.data, verbose)
def tailAreaProbability(self, theStat, verbose=1):
"""Access to :func:`func.tailAreaProbability`
Supply *theStat*, ie the test quantity, and it uses
``self.data`` to make the distribution.
"""
return func.tailAreaProbability(theStat, self.data, verbose)
def compoTestUsingSimulations(self, nSims=100, doIndividualSequences=0, doChiSquare=0, verbose=1):
"""Compositional homogeneity test using a null distribution from simulations.
This does a compositional homogeneity test on each data partition.
The statistic used here is X^2, obtained via
Data.compoChiSquaredTest().
The null distribution of the stat is made using simulations, so of
course you need to provide a tree with a model, with optimized
branch lengths and model parameters. This is a comp homogeneity
test, so the model should be tree-homogeneous.
The analysis usually tests all sequences in the data partition
together (like paup), but you can also 'doIndividualSequences'
(like puzzle). Beware that the latter is a multiple simultaneous
stats test, and so the power may be compromized.
For purposes of comparison, this test can also do compo tests in
the style of PAUP and puzzle, using chi-square to assess
significance. Do this by turning 'doChiSquare' on. The compo test
in PAUP tests all sequences together, while the compo test in
puzzle tests all sequences separately. There are advantages and
disadvantages to the latter-- doing all sequences separately
allows you to identify the worst offenders, but suffers due to the
problems of multiple simultaneous stats tests. There are slight
differences between the computation of the Chi-square in PAUP and
puzzle and the p4 version. The compo test in PAUP (basefreq) does
the chi-squared test, but if sequences are blank it still counts
them in the degrees of freedom; p4 does not count blank sequences
in the degrees of freedom. Puzzle simply uses the row sums, ie
the contributions of each sequence to the total X-squared, and
assesses significance with chi-squared using the number of symbols
minus 1 as the degrees of freedom. Ie for DNA dof=3, for protein
dof=19. Puzzle correctly gets the composition from sequences with
gaps, but does not do the right thing for sequences with
ambiguities like r, y, and so on. P4 does calculate the
composition correctly when there are such ambiguities. So p4 will
give you the same numbers as paup and puzzle for the chi-squared
part as long as you don't have blank sequences or ambiguities like
r and y.
This uses the Data.compoChiSquaredTest() method to get the
stats. See the doc string for that method, where it describes how
zero column sums (ie some character is absent) can be dealt with.
Here, when that method is invoked, 'skipColumnZeros' is turned on,
so that the analysis is robust against data with zero or low
values for some characters.
For example::
# First, do a homog opt, and pickle the optimized tree.
# Here I use a bionj tree, but you could use whatever.
read('d.nex')
a = var.alignments[0]
dm = a.pDistances()
t = dm.bionj()
d = Data()
t.data = d
t.newComp(free=1, spec='empirical')
t.newRMatrix(free=1, spec='ones')
t.setNGammaCat(nGammaCat=4)
t.newGdasrv(free=1, val=0.5)
t.setPInvar(free=0, val=0.0)
t.optLogLike()
t.name = 'homogOpt'
t.tPickle()
# Then, do the test ...
read('homogOpt.p4_tPickle')
t = var.trees[0]
read('d.nex')
d = Data()
t.data = d
t.compoTestUsingSimulations()
# Output would be something like ...
# Composition homogeneity test using simulations.
# P-values are shown.
# Part Num 0
# Part Name all
# -------------------- --------
# All Sequences 0.0000
# Or using more sims for more precision, and also doing the
# Chi-square test for contrast ...
t.compoTestUsingSimulations(nSims=1000, doChiSquare=True)
# Output might be something like ...
# Composition homogeneity test using simulations.
# P-values are shown.
# (P-values from Chi-Square are shown in parens.)
# Part Num 0
# Part Name all
# -------------------- --------
# All Sequences 0.0140
# (Chi-Squared Prob) (0.9933)
It is often the case, as above, that this test will show
significance while the Chi-square test does not.
"""
gm = ['Tree.compoTestUsingSimulations()']
#print "inComp = %s" % self.model.parts[0].comps[0].val
if not self.data:
gm.append("No data. Set the data first.")
raise Glitch, gm
if not self.model:
gm.append("No model. You need to set the model first.")
raise Glitch, gm
self.modelSanityCheck()
if self.model.isHet:
gm.append("The model for this tree is tree-heterogeneous.")
gm.append("This test is not valid for tree-hetero models.")
raise Glitch, gm
# Make a new data object in which to do the sims, so we do not over-write self
#print "a self.data = %s" % self.data
#self.data.dump()
savedData = self.data
self.data = None # This triggers self.deleteCStuff()
self.data = savedData.dupe()
#print "b self.data = %s" % self.data
#self.data.dump()
#raise Glitch, gm
# Check for missing sequences in any of the parts. Missing seq
# nums go in skips, a list of lists.
skips = []
for pNum in range(self.data.nParts):
skips.append([])
for pNum in range(self.data.nParts):
for tNum in range(self.data.nTax):
nSites = pf.partSequenceSitesCount(self.data.parts[pNum].cPart, tNum) # no gaps, no missings
if not nSites:
skips[pNum].append(tNum)
# Get the original stats from self.data.
# compoChiSquaredTest(self, verbose=1, skipColumnZeros=0, useConstantSites=1, skipTaxNums=None, getRows=0)
original = self.data.compoChiSquaredTest(verbose=0,
skipColumnZeros=1,
skipTaxNums=skips,
getRows=doIndividualSequences)
#print "original =", original
# Make some empty lists in which to put our stats
full = []
if doIndividualSequences:
rows = []
for pNum in range(self.data.nParts):
full.append([])
if doIndividualSequences:
onePartRows = []
for i in range(self.data.nTax):
onePartRows.append([])
rows.append(onePartRows)
# Do the sims
for i in range(nSims):
#if i < 5:
# print "%i simComp = %s" % (i, self.model.parts[0].comps[0].val)
self.simulate()
ret = self.data.compoChiSquaredTest(skipColumnZeros=1,
skipTaxNums=skips, getRows=doIndividualSequences, verbose=0)
#print "%i ret=%s" % (i, ret)
for pNum in range(self.data.nParts):
full[pNum].append(ret[pNum][0])
if doIndividualSequences:
for tNum in range(self.data.nTax):
if tNum not in skips[pNum]:
rows[pNum][tNum].append(ret[pNum][3][tNum])
# Find the longest part name length, and heading width, so the output looks nice.
partWid = 8
for p in self.data.parts:
if len(p.name) > partWid:
partWid = len(p.name)
partWid += 2
headWid = 20
for tN in self.data.taxNames:
if len(tN) > headWid:
headWid = len(tN)
headWid += 2
#headSig = '%-' + `headWid` + 's'
headSig = '%' + `headWid - 2` + 's '
# Get the all-sequences tail area probs
partTaps = []
for pNum in range(self.data.nParts):
partTaps.append(func.tailAreaProbability(original[pNum][0], full[pNum], verbose = 0))
# Intro
if verbose:
print "Composition homogeneity test using simulations."
print "P-values are shown."
if doChiSquare:
print "(P-values from Chi-Square are shown in parens.)"
print
# Print the Part Nums and Part Names
if verbose:
print headSig % 'Part Num',
for pNum in range(self.data.nParts):
print string.center('%i' % pNum, partWid),
print
print headSig % 'Part Name',
for pNum in range(self.data.nParts):
print string.center('%s' % self.data.parts[pNum].name, partWid),
print
print headSig % ('-' * (headWid - 2)),
for pNum in range(self.data.nParts):
print string.center('%s' % ('-' * (partWid - 2)), partWid),
print
# Print the all-sequences results
if verbose:
print headSig % 'All Sequences',
for pNum in range(self.data.nParts):
print string.center('%6.4f' % partTaps[pNum], partWid),
print
if doChiSquare:
print headSig % '(Chi-Squared Prob)',
for pNum in range(self.data.nParts):
print string.center('(%6.4f)' % original[pNum][2], partWid),
print
if doIndividualSequences and verbose:
print
#print "Individual sequences"
#print "--------------------"
for tNum in range(self.data.nTax):
print headSig % self.data.taxNames[tNum],
for pNum in range(self.data.nParts):
if tNum not in skips[pNum]:
ret = func.tailAreaProbability(original[pNum][3][tNum], rows[pNum][tNum], verbose = 0)
print string.center('%6.4f' % ret, partWid),
else:
print string.center('%s' % ('-' * 4), partWid),
print
if doChiSquare:
print headSig % ' ',
for pNum in range(self.data.nParts):
dof = self.data.parts[pNum].dim - 1 # degrees of freedom
if tNum not in skips[pNum]:
ret = func.chiSquaredProb(original[pNum][3][tNum], dof)
print string.center('(%6.4f)' % ret, partWid),
else:
print string.center('%s' % ('-' * 4), partWid),
print
# Replace the saved data
self.data = savedData # Since we are replacing an exisiting data, this triggers self.deleteCStuff()
return partTaps[0]
def modelFitTests(self, fName = 'model_fit_tests_out', writeRawStats=0):
"""Do model fit tests on the data.
The two tests are the Goldman-Cox test, and the tree- and model-
based composition fit test. Both require simulations with
optimizations in order to get a null distribution, and those
simulations need to be done before this method. The simulations
should be done with the simsForModelFitTests() method.
Self should have a data and a model attached, and be optimized.
The Goldman-Cox test (Goldman 1993. Statistical tests of models
of DNA substitution. J Mol Evol 36: 182-198.) is a test for
overall fit of the model to the data. It does not work if the
data have gaps or ambiguities.
The tree- and model-based composition test asks the question:
'Does the composition implied by the model fit the data?' If the
model is homogeneous and empirical comp is used, then this is the
same as the chi-square test except that the null distribution
comes from simulations, not from the chi-square distribution. In
that case only the question is, additionally, 'Are the data
homogeneous in composition?', ie the same question asked by the
chi-square test. However, the data might be heterogeneous, and
the model might be heterogeneous over the tree; the tree- and
model-based composition fit test can ask whether the heterogeneous
model fits the heterogeneous data. The composition is tested in
each data partition, separately. The test is done both overall,
ie for all the sequences together, and for individual sequences.
If you just want a compo homogeneity test with empirical
homogeneous comp, try the compoTestUsingSimulations() method-- its
way faster, because there are not optimizations in the sims part.
Output is verbose, to a file."""
gm = ['Tree.modelFitTests()']
self.calcLogLike(verbose=0)
doOut = True # Usually True. Set to False for debugging, experimentation, getting individual stats, etc
# We can't do the Goldman-Cox test if there are any gaps or
# ambiguities.
doGoldmanCox = True
for a in self.data.alignments:
if a.hasGapsOrAmbiguities():
doGoldmanCox = False
break
#print "test doGoldmanCox = %s" % doGoldmanCox
rawFName = '%s_raw.py' % fName
#flob = sys.stderr
#fRaw = sys.stderr
if doOut:
flob = file(fName, 'w')
else:
flob = None
if writeRawStats:
fRaw = file(rawFName, 'w')
else:
fRaw = None
#######################
# Goldman-Cox stats
#######################
# For a two-part data analysis, the first few lines of the
# sims_GoldmanStats_* file will be like the following. Its in
# groups of 3-- the first one for all parts together (part number
# -1), and the next lines for separate parts.
## # part unconstr L log like Goldman-Cox stat
## -1 -921.888705 -1085.696919 163.808215
## 0 -357.089057 -430.941958 73.852901
## 1 -564.799648 -654.754962 89.955314
## -1 -952.063037 -1130.195799 178.132761
## 0 -362.164119 -439.709824 77.545705
## ... and so on.
# For a one-part analysis, it will be the same except that one sim
# gets only one line, starting with zero.
if doGoldmanCox:
goldmanOverallSimStats = []
if self.data.nParts > 1:
goldmanIndividualSimStats = []
for partNum in range(self.data.nParts):
goldmanIndividualSimStats.append([])
import glob
goldmanFNames = glob.glob('sims_GoldmanStats_*')
#print "nParts=%s" % self.data.nParts
#print goldmanFNames
for fName1 in goldmanFNames:
f2 = open(fName1)
aLine = f2.readline()
if not aLine:
gm.append("Empty file %s" % fName1)
raise Glitch, gm
if aLine[0] != '#':
gm.append("Expecting a '#' as the first character in file %s" % fName1)
raise Glitch, gm
aLine = f2.readline()
#print "a got line %s" % aLine,
while aLine:
if self.data.nParts > 1:
splitLine = aLine.split()
if len(splitLine) != 4:
gm.append("Bad line in Goldman stats file %s" % fName1)
gm.append("'%s'" % aLine)
raise Glitch, gm
if int(splitLine[0]) != -1:
gm.append("Bad line in Goldman stats file %s" % fName1)
gm.append("First item should be -1")
gm.append("'%s'" % aLine)
raise Glitch, gm
#print splitLine[-1]
goldmanOverallSimStats.append(float(splitLine[-1]))
aLine = f2.readline()
#print "b got line %s" % aLine,
if not aLine:
gm.append("Premature end to file %s" % fName1)
raise Glitch, gm
for partNum in range(self.data.nParts):
splitLine = aLine.split()
#print "partNum %i, splitLine=%s" % (partNum, splitLine)
if len(splitLine) != 4:
gm.append("Bad line in Goldman stats file %s" % fName1)
gm.append("'%s'" % aLine)
raise Glitch, gm
try:
splitLine[0] = int(splitLine[0])
except ValueError:
gm.append("Bad line in Goldman stats file %s" % fName1)
gm.append("First item should be the partNum %i" % partNum)
gm.append("'%s'" % aLine)
raise Glitch, gm
if splitLine[0] != partNum:
gm.append("Bad line in Goldman stats file %s" % fName1)
gm.append("First item should be the partNum %i" % partNum)
gm.append("'%s'" % aLine)
raise Glitch, gm
#for taxNum in range(self.data.nTax):
# print splitLine[taxNum + 1]
#print splitLine[-1]
if self.data.nParts == 1:
goldmanOverallSimStats.append(float(splitLine[-1]))
else:
goldmanIndividualSimStats[partNum].append(float(splitLine[-1]))
aLine = f2.readline()
#print "c got line %s" % aLine,
f2.close()
#print "goldmanOverallSimStats =", goldmanOverallSimStats
#print "goldmanIndividualSimStats =", goldmanIndividualSimStats
#sys.exit()
if doOut:
flob.write('Model fit tests\n===============\n\n')
flob.write('The data that we are testing have %i taxa,\n' % self.data.nTax)
if len(self.data.alignments) == 1:
flob.write('1 alignment, ')
else:
flob.write('%i alignments, ' % len(self.data.alignments))
if self.data.nParts == 1:
flob.write('and 1 data partition.\n')
else:
flob.write('and %i data partitions.\n' % self.data.nParts)
flob.write('The lengths of those partitions are as follows:\n')
flob.write(' partNum nChar \n')
for i in range(self.data.nParts):
flob.write(' %3i %5i\n' % (i, self.data.parts[i].nChar))
self.data.calcUnconstrainedLogLikelihood2()
if doOut:
flob.write("\nThe unconstrained likelihood is %f\n" % self.data.unconstrainedLogLikelihood)
flob.write('(This is the partition-by-partition unconstrained log likelihood, \n')
flob.write('ie the sum of the unconstrained log likes from each partition separately, \n')
flob.write('and so will not be the same as that given by PAUP, if the data are partitioned.)\n')
flob.write('\n\nGoldman-Cox test for overall model fit\n')
flob.write ('======================================\n')
flob.write('The log likelihood for these data for this tree is %f\n' % self.logLike)
flob.write('The unconstrained log likelihood for these data is %f\n' % self.data.unconstrainedLogLikelihood)
originalGoldmanCoxStat = self.data.unconstrainedLogLikelihood - self.logLike
if doOut:
flob.write('The Goldman-Cox statistic for the original data is the difference, %f\n' % originalGoldmanCoxStat)
if self.data.nParts > 1:
flob.write('(The unconstrained log likelihood for these data is calculated partition by partition.)\n')
flob.write('\n')
if self.data.nParts > 1:
originalGoldmanCoxStatsByPart = []
if doOut:
flob.write('Stats by partition.\n')
flob.write('part\t unconstrLogL\t log like \tGoldman-Cox stat\n')
flob.write('----\t ----------\t -------- \t----------------\n')
for partNum in range(self.data.nParts):
unc = pf.getUnconstrainedLogLike(self.data.parts[partNum].cPart)
like = pf.p4_partLogLike(self.cTree, self.data.parts[partNum].cPart, partNum, 0)
diff = unc - like
if doOut:
flob.write(' %i\t%f\t%f\t %f\n' % (partNum, unc, like, diff))
originalGoldmanCoxStatsByPart.append(diff)
# Do the overall stat
nSims = len(goldmanOverallSimStats)
if doOut: flob.write('\nThere were %i simulations.\n\n' % nSims)
if writeRawStats:
fRaw.write('# Goldman-Cox null distributions.\n')
if self.data.nParts > 1:
fRaw.write('# Simulation stats for overall data, ie for all data partitions combined.\n')
else:
fRaw.write('# Simulation stats.\n')
fRaw.write('goldman_cox_overall = %s\n' % goldmanOverallSimStats)
if self.data.nParts > 1:
for partNum in range(self.data.nParts):
fRaw.write('# Simulation stats for data partition %i\n' % partNum)
fRaw.write('goldman_cox_part%i = %s\n' % (partNum, goldmanIndividualSimStats[partNum]))
prob = func.tailAreaProbability(originalGoldmanCoxStat, goldmanOverallSimStats, verbose=0)
if doOut:
flob.write( '\n Overall Goldman-Cox test: ')
if prob <= 0.05:
flob.write('%13s' % "Doesn't fit.")
else:
flob.write('%13s' % 'Fits.')
flob.write(' P = %5.3f\n' % prob)
if self.data.nParts > 1:
if doOut: flob.write(' Tests for individual data partitions:\n')
for partNum in range(self.data.nParts):
prob = func.tailAreaProbability(originalGoldmanCoxStatsByPart[partNum],
goldmanIndividualSimStats[partNum], verbose=0)
if doOut:
flob.write( ' Part %-2i: ' % partNum)
if prob <= 0.05:
flob.write('%13s' % 'Doesn\'t fit.')
else:
flob.write('%13s' % 'Fits.')
flob.write(' P = %5.3f\n' % prob)
#########################
# COMPOSITION
#########################
statsHashList = []
for pNum in range(self.data.nParts):
h = {}
statsHashList.append(h)
h['individualNSites'] = []
h['observedIndividualCounts'] = []
for j in range(self.data.nTax):
#print pf.partSequenceSitesCount(self.data.parts[pNum].cPart, j)
h['individualNSites'].append(pf.partSequenceSitesCount(self.data.parts[pNum].cPart, j)) # no gaps or qmarks
#print self.data.parts[pNum].composition([j])
h['observedIndividualCounts'].append(self.data.parts[pNum].composition([j]))
# The line above is temporarily composition, not counts
# pf.expectedCompositionCounts returns a tuple of tuples
# representing the counts of the nodes in proper alignment order.
h['expectedIndividualCounts'] = list(pf.p4_expectedCompositionCounts(self.cTree, pNum)) # alignment order
# At the moment, h['observedIndividualCounts'] has composition,
# not counts. So multiply by h['individualNSites']
for i in range(self.data.nTax):
for j in range(self.data.parts[pNum].dim):
h['observedIndividualCounts'][i][j] *= h['individualNSites'][i]
# We will want to skip any sequences composed of all gaps
skipTaxNums = []
for pNum in range(self.data.nParts):
stn = []
for tNum in range(self.data.nTax):
if not statsHashList[pNum]['individualNSites'][tNum]:
stn.append(tNum)
skipTaxNums.append(stn)
#print "skipTaxNums = %s" % skipTaxNums
# Do the boring old compo chi square test.
if doOut: flob.write(longMessage1) # explanation ...
for pNum in range(self.data.nParts):
h = statsHashList[pNum]
# Can't use func.xSquared(), because there might be column
# zeros.
#print "observedIndividualCounts = %s' % h['observedIndividualCounts"]
nRows = len(h['observedIndividualCounts'])
nCols = len(h['observedIndividualCounts'][0])
theSumOfRows = func._sumOfRows(h['observedIndividualCounts']) # I could have just used nSites, above
theSumOfCols = func._sumOfColumns(h['observedIndividualCounts'])
#print theSumOfCols
isOk = 1
columnZeros = []
#for j in range(len(theSumOfRows)):
# if theSumOfRows[j] == 0.0:
# gm.append("Zero in a row sum. Programming error.")
# raise Glitch, gm
for j in range(len(theSumOfCols)):
if theSumOfCols[j] <= 0.0:
columnZeros.append(j)
theExpected = func._expected(theSumOfRows, theSumOfCols)
#print "theExpected = %s" % theExpected
#print "columnZeros = %s" % columnZeros
xSq = 0.0
for rowNum in range(nRows):
if rowNum in skipTaxNums[pNum]:
pass
else:
xSq_row = 0.0
for colNum in range(nCols):
if colNum in columnZeros:
pass
else:
theDiff = h['observedIndividualCounts'][rowNum][colNum] - theExpected[rowNum][colNum]
xSq_row += (theDiff * theDiff) / theExpected[rowNum][colNum]
xSq += xSq_row
dof = (nCols - len(columnZeros) - 1) * (nRows - len(skipTaxNums[pNum]) - 1)
prob = func.chiSquaredProb(xSq, dof)
if doOut: flob.write(' Part %i: Chi-square = %f, (dof=%i) P = %f\n' % (pNum, xSq, dof, prob))
for pNum in range(self.data.nParts):
h = statsHashList[pNum]
h['overallStat'] = 0.0
h['individualStats'] = [0.0] * self.data.nTax
for i in range(self.data.nTax):
if i in skipTaxNums[pNum]:
pass # h['individualStats'] stays at zeros
else:
for j in range(self.data.parts[pNum].dim):
# Avoid dividing by Zero.
if h['expectedIndividualCounts'][i][j]:
dif = h['observedIndividualCounts'][i][j] - h['expectedIndividualCounts'][i][j]
h['individualStats'][i] += ((dif * dif) /h['expectedIndividualCounts'][i][j])
h['overallStat'] += h['individualStats'][i]
h['overallSimStats'] = []
h['individualSimStats'] = []
for i in range(self.data.nTax):
h['individualSimStats'].append([])
if 0:
print "h['individualNSites'] = %s" % h['individualNSites']
print "h['observedIndividualCounts'] = %s" % h['observedIndividualCounts']
print "h['expectedIndividualCounts'] = %s" % h['expectedIndividualCounts']
print "h['overallStat'] = %s" % h['overallStat']
print "h['individualStats'] = %s" % h['individualStats']
raise Glitch, gm
import glob
compoFNames = glob.glob('sims_CompStats_*')
#print compoFNames
for fName1 in compoFNames:
f2 = open(fName1)
aLine = f2.readline()
if not aLine:
gm.append("Empty file %s" % fName1)
raise Glitch, gm
#print "a got line %s" % aLine,
while aLine:
for partNum in range(self.data.nParts):
h = statsHashList[partNum]
splitLine = aLine.split()
if len(splitLine) != (self.data.nTax + 2):
gm.append("Bad line in composition stats file %s" % fName1)
gm.append("'%s'" % aLine)
raise Glitch, gm
if int(splitLine[0]) != partNum:
gm.append("Bad line in composition stats file %s" % fName1)
gm.append("First item should be the partNum %i" % partNum)
gm.append("'%s'" % aLine)
raise Glitch, gm
#for taxNum in range(self.data.nTax):
# print splitLine[taxNum + 1]
#print splitLine[-1]
h['overallSimStats'].append(float(splitLine[-1]))
for i in range(self.data.nTax):
h['individualSimStats'][i].append(float(splitLine[i + 1]))
#raise Glitch, gm
aLine = f2.readline()
if not aLine:
break
#print "b got line %s" % aLine,
f2.close()
nSims = len(statsHashList[0]['overallSimStats'])
if doOut:
flob.write(longMessage2) # Explain tree- and model-based compo fit stat, X^2_m
flob.write( ' %i simulation reps were used.\n\n' % nSims)
spacer1 = ' ' * 10
for partNum in range(self.data.nParts):
h = statsHashList[partNum]
if doOut:
flob.write('Part %-2i:\n-------\n\n' % partNum)
flob.write('Statistics from the original data\n')
flob.write('%s%30s: %f\n' % (spacer1, 'Overall observed stat', h['overallStat']))
flob.write('%s%30s:\n' % (spacer1, 'Stats for individual taxa'))
for taxNum in range(self.data.nTax):
if taxNum not in skipTaxNums[partNum]:
flob.write('%s%30s: %f\n' % (spacer1, self.data.taxNames[taxNum], h['individualStats'][taxNum]))
else:
flob.write('%s%30s: skipped\n' % (spacer1, self.data.taxNames[taxNum]))
flob.write('\nAssessment of fit from null distribution from %i simulations\n' % nSims)
flob.write('%s%30s: ' % (spacer1, 'Overall'))
prob = func.tailAreaProbability(h['overallStat'], h['overallSimStats'], verbose=0)
if doOut:
if prob <= 0.05:
flob.write('%13s' % 'Doesn\'t fit.')
else:
flob.write('%13s' % 'Fits.')
flob.write(' P = %5.3f\n' % prob)
#############
theRet= prob
#############
for taxNum in range(self.data.nTax):
if doOut: flob.write('%s%30s: ' % (spacer1, self.data.taxNames[taxNum]))
if taxNum in skipTaxNums[partNum]:
if doOut: flob.write('%13s\n' % 'skipped.')
else:
prob = func.tailAreaProbability(h['individualStats'][taxNum],
h['individualSimStats'][taxNum], verbose=0)
if doOut:
if prob <= 0.05:
flob.write('%13s' % "Doesn't fit.")
else:
flob.write('%13s' % 'Fits.')
flob.write(' P = %5.3f\n' % prob)
if writeRawStats:
fRaw.write('#\n# Tree and model based composition fit test\n')
fRaw.write('# =========================================\n')
fRaw.write('# Simulation statistics, ie the null distributions\n\n')
fRaw.write('# Part %i:\n' % partNum)
fRaw.write('part%i_overall_compo_null = %s\n' % (partNum, h['overallSimStats']))
for taxNum in range(self.data.nTax):
fRaw.write('part%i_%s_compo_null = %s\n' % (partNum,
_fixFileName(self.data.taxNames[taxNum]),
h['individualSimStats'][taxNum]))
if flob and flob != sys.stdout: # Yes, it is possible to close sys.stdout
flob.close()
if fRaw and fRaw != sys.stdout:
fRaw.close()
return theRet
def compoTestUsingSimulations(self, nSims=100, doIndividualSequences=0, doChiSquare=0, verbose=1):
"""Compositional homogeneity test using a null distribution from simulations.
This does a compositional homogeneity test on each data partition.
The statistic used here is X^2, obtained via
Data.compoChiSquaredTest().
The null distribution of the stat is made using simulations, so of
course you need to provide a tree with a model, with optimized
branch lengths and model parameters. This is a comp homogeneity
test, so the model should be tree-homogeneous.
The analysis usually tests all sequences in the data partition
together (like paup), but you can also 'doIndividualSequences'
(like puzzle). Beware that the latter is a multiple simultaneous
stats test, and so the power may be compromized.
For purposes of comparison, this test can also do compo tests in
the style of PAUP and puzzle, using chi-square to assess
significance. Do this by turning 'doChiSquare' on. The compo test
in PAUP tests all sequences together, while the compo test in
puzzle tests all sequences separately. There are advantages and
disadvantages to the latter-- doing all sequences separately
allows you to identify the worst offenders, but suffers due to the
problems of multiple simultaneous stats tests. There are slight
differences between the computation of the Chi-square in PAUP and
puzzle and the p4 version. The compo test in PAUP (basefreq) does
the chi-squared test, but if sequences are blank it still counts
them in the degrees of freedom; p4 does not count blank sequences
in the degrees of freedom. Puzzle simply uses the row sums, ie
the contributions of each sequence to the total X-squared, and
assesses significance with chi-squared using the number of symbols
minus 1 as the degrees of freedom. Ie for DNA dof=3, for protein
dof=19. Puzzle correctly gets the composition from sequences with
gaps, but does not do the right thing for sequences with
ambiguities like r, y, and so on. P4 does calculate the
composition correctly when there are such ambiguities. So p4 will
give you the same numbers as paup and puzzle for the chi-squared
part as long as you don't have blank sequences or ambiguities like
r and y.
This uses the Data.compoChiSquaredTest() method to get the
stats. See the doc string for that method, where it describes how
zero column sums (ie some character is absent) can be dealt with.
Here, when that method is invoked, 'skipColumnZeros' is turned on,
so that the analysis is robust against data with zero or low
values for some characters. """
gm = ['Tree.compoTestUsingSimulations()']
#print "inComp = %s" % self.model.parts[0].comps[0].val
if not self.data:
gm.append("No data. Set the data first.")
raise Glitch, gm
if not self.model:
gm.append("No model. You need to set the model first.")
raise Glitch, gm
self.modelSanityCheck()
if self.model.isHet:
gm.append("The model for this tree is tree-heterogeneous.")
gm.append("This test is not valid for tree-hetero models.")
raise Glitch, gm
# Make a new data object in which to do the sims, so we do not over-write self
#print "a self.data = %s" % self.data
#self.data.dump()
savedData = self.data
self.data = None # This triggers self.deleteCStuff()
self.data = savedData.dupe()
#print "b self.data = %s" % self.data
#self.data.dump()
#raise Glitch, gm
# Check for missing sequences in any of the parts. Missing seq
# nums go in skips, a list of lists.
skips = []
for pNum in range(self.data.nParts):
skips.append([])
for pNum in range(self.data.nParts):
for tNum in range(self.data.nTax):
nSites = pf.partSequenceSitesCount(self.data.parts[pNum].cPart, tNum) # no gaps, no missings
if not nSites:
skips[pNum].append(tNum)
# Get the original stats from self.data.
# compoChiSquaredTest(self, verbose=1, skipColumnZeros=0, useConstantSites=1, skipTaxNums=None, getRows=0)
original = self.data.compoChiSquaredTest(verbose=0,
skipColumnZeros=1,
skipTaxNums=skips,
getRows=doIndividualSequences)
#print "original =", original
# Make some empty lists in which to put our stats
full = []
if doIndividualSequences:
rows = []
for pNum in range(self.data.nParts):
full.append([])
if doIndividualSequences:
onePartRows = []
for i in range(self.data.nTax):
onePartRows.append([])
rows.append(onePartRows)
# Do the sims
for i in range(nSims):
#if i < 5:
# print "%i simComp = %s" % (i, self.model.parts[0].comps[0].val)
self.simulate()
ret = self.data.compoChiSquaredTest(skipColumnZeros=1,
skipTaxNums=skips, getRows=doIndividualSequences, verbose=0)
#print "%i ret=%s" % (i, ret)
for pNum in range(self.data.nParts):
full[pNum].append(ret[pNum][0])
if doIndividualSequences:
for tNum in range(self.data.nTax):
if tNum not in skips[pNum]:
rows[pNum][tNum].append(ret[pNum][3][tNum])
# Find the longest part name length, and heading width, so the output looks nice.
partWid = 8
for p in self.data.parts:
if len(p.name) > partWid:
partWid = len(p.name)
partWid += 2
headWid = 20
for tN in self.data.taxNames:
if len(tN) > headWid:
headWid = len(tN)
headWid += 2
#headSig = '%-' + `headWid` + 's'
headSig = '%' + `headWid - 2` + 's '
# Get the all-sequences tail area probs
partTaps = []
for pNum in range(self.data.nParts):
partTaps.append(func.tailAreaProbability(original[pNum][0], full[pNum], verbose = 0))
# Intro
if verbose:
print "Composition homogeneity test using simulations."
print "P-values are shown."
if doChiSquare:
print "(P-values from Chi-Square are shown in parens.)"
print
# Print the Part Nums and Part Names
if verbose:
print headSig % 'Part Num',
for pNum in range(self.data.nParts):
print string.center('%i' % pNum, partWid),
print
print headSig % 'Part Name',
for pNum in range(self.data.nParts):
print string.center('%s' % self.data.parts[pNum].name, partWid),
print
print headSig % ('-' * (headWid - 2)),
for pNum in range(self.data.nParts):
print string.center('%s' % ('-' * (partWid - 2)), partWid),
print
# Print the all-sequences results
if verbose:
print headSig % 'All Sequences',
for pNum in range(self.data.nParts):
print string.center('%6.4f' % partTaps[pNum], partWid),
print
if doChiSquare:
print headSig % '(Chi-Squared Prob)',
for pNum in range(self.data.nParts):
print string.center('(%6.4f)' % original[pNum][2], partWid),
print
if doIndividualSequences and verbose:
print
#print "Individual sequences"
#print "--------------------"
for tNum in range(self.data.nTax):
print headSig % self.data.taxNames[tNum],
for pNum in range(self.data.nParts):
if tNum not in skips[pNum]:
ret = func.tailAreaProbability(original[pNum][3][tNum], rows[pNum][tNum], verbose = 0)
print string.center('%6.4f' % ret, partWid),
else:
print string.center('%s' % ('-' * 4), partWid),
print
if doChiSquare:
print headSig % ' ',
for pNum in range(self.data.nParts):
dof = self.data.parts[pNum].dim - 1 # degrees of freedom
if tNum not in skips[pNum]:
ret = func.chiSquaredProb(original[pNum][3][tNum], dof)
print string.center('(%6.4f)' % ret, partWid),
else:
print string.center('%s' % ('-' * 4), partWid),
print
# Replace the saved data
self.data = savedData # Since we are replacing an exisiting data, this triggers self.deleteCStuff()
return partTaps[0]
def modelFitTests(self, fName = 'model_fit_tests_out', writeRawStats=0):
"""Do model fit tests on the data.
The two tests are the Goldman-Cox test, and the tree- and model-
based composition fit test. Both require simulations with
optimizations in order to get a null distribution, and those
simulations need to be done before this method. The simulations
should be done with the simsForModelFitTests() method.
Self should have a data and a model attached, and be optimized.
The Goldman-Cox test (Goldman 1993. Statistical tests of models
of DNA substitution. J Mol Evol 36: 182-198.) is a test for
overall fit of the model to the data. It does not work if the
data have gaps or ambiguities.
The tree- and model-based composition test asks the question:
'Does the composition implied by the model fit the data?' If the
model is homogeneous and empirical comp is used, then this is the
same as the chi-square test except that the null distribution
comes from simulations, not from the chi-square distribution. In
that case only the question is, additionally, 'Are the data
homogeneous in composition?', ie the same question asked by the
chi-square test. However, the data might be heterogeneous, and
the model might be heterogeneous over the tree; the tree- and
model-based composition fit test can ask whether the heterogeneous
model fits the heterogeneous data. The composition is tested in
each data partition, separately. The test is done both overall,
ie for all the sequences together, and for individual sequences.
If you just want a compo homogeneity test with empirical
homogeneous comp, try the compoTestUsingSimulations() method-- its
way faster, because there are not optimizations in the sims part.
Output is verbose, to a file."""
gm = ['Tree.modelFitTests()']
self.calcLogLike(verbose=0)
doOut = True # Usually True. Set to False for debugging, experimentation, getting individual stats, etc
# We can't do the Goldman-Cox test if there are any gaps or
# ambiguities.
doGoldmanCox = True
for a in self.data.alignments:
if a.hasGapsOrAmbiguities():
doGoldmanCox = False
break
#print "test doGoldmanCox = %s" % doGoldmanCox
rawFName = '%s_raw.py' % fName
#flob = sys.stderr
#fRaw = sys.stderr
if doOut:
flob = file(fName, 'w')
else:
flob = None
if writeRawStats:
fRaw = file(rawFName, 'w')
else:
fRaw = None
#######################
# Goldman-Cox stats
#######################
# For a two-part data analysis, the first few lines of the
# sims_GoldmanStats_* file will be like the following. Its in
# groups of 3-- the first one for all parts together (part number
# -1), and the next lines for separate parts.
## # part unconstr L log like Goldman-Cox stat
## -1 -921.888705 -1085.696919 163.808215
## 0 -357.089057 -430.941958 73.852901
## 1 -564.799648 -654.754962 89.955314
## -1 -952.063037 -1130.195799 178.132761
## 0 -362.164119 -439.709824 77.545705
## ... and so on.
# For a one-part analysis, it will be the same except that one sim
# gets only one line, starting with zero.
if doGoldmanCox:
goldmanOverallSimStats = []
if self.data.nParts > 1:
goldmanIndividualSimStats = []
for partNum in range(self.data.nParts):
goldmanIndividualSimStats.append([])
import glob
goldmanFNames = glob.glob('sims_GoldmanStats_*')
#print "nParts=%s" % self.data.nParts
#print goldmanFNames
for fName1 in goldmanFNames:
f2 = open(fName1)
aLine = f2.readline()
if not aLine:
gm.append("Empty file %s" % fName1)
raise Glitch, gm
if aLine[0] != '#':
gm.append("Expecting a '#' as the first character in file %s" % fName1)
raise Glitch, gm
aLine = f2.readline()
#print "a got line %s" % aLine,
while aLine:
if self.data.nParts > 1:
splitLine = aLine.split()
if len(splitLine) != 4:
gm.append("Bad line in Goldman stats file %s" % fName1)
gm.append("'%s'" % aLine)
raise Glitch, gm
if int(splitLine[0]) != -1:
gm.append("Bad line in Goldman stats file %s" % fName1)
gm.append("First item should be -1")
gm.append("'%s'" % aLine)
raise Glitch, gm
#print splitLine[-1]
goldmanOverallSimStats.append(float(splitLine[-1]))
aLine = f2.readline()
#print "b got line %s" % aLine,
if not aLine:
gm.append("Premature end to file %s" % fName1)
raise Glitch, gm
for partNum in range(self.data.nParts):
splitLine = aLine.split()
#print "partNum %i, splitLine=%s" % (partNum, splitLine)
if len(splitLine) != 4:
gm.append("Bad line in Goldman stats file %s" % fName1)
gm.append("'%s'" % aLine)
raise Glitch, gm
try:
splitLine[0] = int(splitLine[0])
except ValueError:
gm.append("Bad line in Goldman stats file %s" % fName1)
gm.append("First item should be the partNum %i" % partNum)
gm.append("'%s'" % aLine)
raise Glitch, gm
if splitLine[0] != partNum:
gm.append("Bad line in Goldman stats file %s" % fName1)
gm.append("First item should be the partNum %i" % partNum)
gm.append("'%s'" % aLine)
raise Glitch, gm
#for taxNum in range(self.data.nTax):
# print splitLine[taxNum + 1]
#print splitLine[-1]
if self.data.nParts == 1:
goldmanOverallSimStats.append(float(splitLine[-1]))
else:
goldmanIndividualSimStats[partNum].append(float(splitLine[-1]))
aLine = f2.readline()
#print "c got line %s" % aLine,
f2.close()
#print "goldmanOverallSimStats =", goldmanOverallSimStats
#print "goldmanIndividualSimStats =", goldmanIndividualSimStats
#sys.exit()
if doOut:
flob.write('Model fit tests\n===============\n\n')
flob.write('The data that we are testing have %i taxa,\n' % self.data.nTax)
if len(self.data.alignments) == 1:
flob.write('1 alignment, ')
else:
flob.write('%i alignments, ' % len(self.data.alignments))
if self.data.nParts == 1:
flob.write('and 1 data partition.\n')
else:
flob.write('and %i data partitions.\n' % self.data.nParts)
flob.write('The lengths of those partitions are as follows:\n')
flob.write(' partNum nChar \n')
for i in range(self.data.nParts):
flob.write(' %3i %5i\n' % (i, self.data.parts[i].nChar))
self.data.calcUnconstrainedLogLikelihood2()
if doOut:
flob.write("\nThe unconstrained likelihood is %f\n" % self.data.unconstrainedLogLikelihood)
flob.write('(This is the partition-by-partition unconstrained log likelihood, \n')
flob.write('ie the sum of the unconstrained log likes from each partition separately, \n')
flob.write('and so will not be the same as that given by PAUP, if the data are partitioned.)\n')
flob.write('\n\nGoldman-Cox test for overall model fit\n')
flob.write ('======================================\n')
flob.write('The log likelihood for these data for this tree is %f\n' % self.logLike)
flob.write('The unconstrained log likelihood for these data is %f\n' % self.data.unconstrainedLogLikelihood)
originalGoldmanCoxStat = self.data.unconstrainedLogLikelihood - self.logLike
if doOut:
flob.write('The Goldman-Cox statistic for the original data is the difference, %f\n' % originalGoldmanCoxStat)
if self.data.nParts > 1:
flob.write('(The unconstrained log likelihood for these data is calculated partition by partition.)\n')
flob.write('\n')
if self.data.nParts > 1:
originalGoldmanCoxStatsByPart = []
if doOut:
flob.write('Stats by partition.\n')
flob.write('part\t unconstrLogL\t log like \tGoldman-Cox stat\n')
flob.write('----\t ----------\t -------- \t----------------\n')
for partNum in range(self.data.nParts):
unc = pf.getUnconstrainedLogLike(self.data.parts[partNum].cPart)
like = pf.p4_partLogLike(self.cTree, self.data.parts[partNum].cPart, partNum, 0)
diff = unc - like
if doOut:
flob.write(' %i\t%f\t%f\t %f\n' % (partNum, unc, like, diff))
originalGoldmanCoxStatsByPart.append(diff)
# Do the overall stat
nSims = len(goldmanOverallSimStats)
if doOut: flob.write('\nThere were %i simulations.\n\n' % nSims)
if writeRawStats:
fRaw.write('# Goldman-Cox null distributions.\n')
if self.data.nParts > 1:
fRaw.write('# Simulation stats for overall data, ie for all data partitions combined.\n')
else:
fRaw.write('# Simulation stats.\n')
fRaw.write('goldman_cox_overall = %s\n' % goldmanOverallSimStats)
if self.data.nParts > 1:
for partNum in range(self.data.nParts):
fRaw.write('# Simulation stats for data partition %i\n' % partNum)
fRaw.write('goldman_cox_part%i = %s\n' % (partNum, goldmanIndividualSimStats[partNum]))
prob = func.tailAreaProbability(originalGoldmanCoxStat, goldmanOverallSimStats, verbose=0)
if doOut:
flob.write( '\n Overall Goldman-Cox test: ')
if prob <= 0.05:
flob.write('%13s' % "Doesn't fit.")
else:
flob.write('%13s' % 'Fits.')
flob.write(' P = %5.3f\n' % prob)
if self.data.nParts > 1:
if doOut: flob.write(' Tests for individual data partitions:\n')
for partNum in range(self.data.nParts):
prob = func.tailAreaProbability(originalGoldmanCoxStatsByPart[partNum],
goldmanIndividualSimStats[partNum], verbose=0)
if doOut:
flob.write( ' Part %-2i: ' % partNum)
if prob <= 0.05:
flob.write('%13s' % 'Doesn\'t fit.')
else:
flob.write('%13s' % 'Fits.')
flob.write(' P = %5.3f\n' % prob)
#########################
# COMPOSITION
#########################
statsHashList = []
for pNum in range(self.data.nParts):
h = {}
statsHashList.append(h)
h['individualNSites'] = []
h['observedIndividualCounts'] = []
for j in range(self.data.nTax):
#print pf.partSequenceSitesCount(self.data.parts[pNum].cPart, j)
h['individualNSites'].append(pf.partSequenceSitesCount(self.data.parts[pNum].cPart, j)) # no gaps or qmarks
#print self.data.parts[pNum].composition([j])
h['observedIndividualCounts'].append(self.data.parts[pNum].composition([j]))
# The line above is temporarily composition, not counts
# pf.expectedCompositionCounts returns a tuple of tuples
# representing the counts of the nodes in proper alignment order.
h['expectedIndividualCounts'] = list(pf.p4_expectedCompositionCounts(self.cTree, pNum)) # alignment order
# At the moment, h['observedIndividualCounts'] has composition,
# not counts. So multiply by h['individualNSites']
for i in range(self.data.nTax):
for j in range(self.data.parts[pNum].dim):
h['observedIndividualCounts'][i][j] *= h['individualNSites'][i]
# We will want to skip any sequences composed of all gaps
skipTaxNums = []
for pNum in range(self.data.nParts):
stn = []
for tNum in range(self.data.nTax):
if not statsHashList[pNum]['individualNSites'][tNum]:
stn.append(tNum)
skipTaxNums.append(stn)
#print "skipTaxNums = %s" % skipTaxNums
# Do the boring old compo chi square test.
if doOut: flob.write(longMessage1) # explanation ...
for pNum in range(self.data.nParts):
h = statsHashList[pNum]
# Can't use func.xSquared(), because there might be column
# zeros.
#print "observedIndividualCounts = %s' % h['observedIndividualCounts"]
nRows = len(h['observedIndividualCounts'])
nCols = len(h['observedIndividualCounts'][0])
theSumOfRows = func._sumOfRows(h['observedIndividualCounts']) # I could have just used nSites, above
theSumOfCols = func._sumOfColumns(h['observedIndividualCounts'])
#print theSumOfCols
isOk = 1
columnZeros = []
#for j in range(len(theSumOfRows)):
# if theSumOfRows[j] == 0.0:
# gm.append("Zero in a row sum. Programming error.")
# raise Glitch, gm
for j in range(len(theSumOfCols)):
if theSumOfCols[j] <= 0.0:
columnZeros.append(j)
theExpected = func._expected(theSumOfRows, theSumOfCols)
#print "theExpected = %s" % theExpected
#print "columnZeros = %s" % columnZeros
xSq = 0.0
for rowNum in range(nRows):
if rowNum in skipTaxNums[pNum]:
pass
else:
xSq_row = 0.0
for colNum in range(nCols):
if colNum in columnZeros:
pass
else:
theDiff = h['observedIndividualCounts'][rowNum][colNum] - theExpected[rowNum][colNum]
xSq_row += (theDiff * theDiff) / theExpected[rowNum][colNum]
xSq += xSq_row
dof = (nCols - len(columnZeros) - 1) * (nRows - len(skipTaxNums[pNum]) - 1)
prob = func.chiSquaredProb(xSq, dof)
if doOut: flob.write(' Part %i: Chi-square = %f, (dof=%i) P = %f\n' % (pNum, xSq, dof, prob))
for pNum in range(self.data.nParts):
h = statsHashList[pNum]
h['overallStat'] = 0.0
h['individualStats'] = [0.0] * self.data.nTax
for i in range(self.data.nTax):
if i in skipTaxNums[pNum]:
pass # h['individualStats'] stays at zeros
else:
for j in range(self.data.parts[pNum].dim):
# Avoid dividing by Zero.
if h['expectedIndividualCounts'][i][j]:
dif = h['observedIndividualCounts'][i][j] - h['expectedIndividualCounts'][i][j]
h['individualStats'][i] += ((dif * dif) /h['expectedIndividualCounts'][i][j])
h['overallStat'] += h['individualStats'][i]
h['overallSimStats'] = []
h['individualSimStats'] = []
for i in range(self.data.nTax):
h['individualSimStats'].append([])
if 0:
print "h['individualNSites'] = %s" % h['individualNSites']
print "h['observedIndividualCounts'] = %s" % h['observedIndividualCounts']
print "h['expectedIndividualCounts'] = %s" % h['expectedIndividualCounts']
print "h['overallStat'] = %s" % h['overallStat']
print "h['individualStats'] = %s" % h['individualStats']
raise Glitch, gm
import glob
compoFNames = glob.glob('sims_CompStats_*')
#print compoFNames
for fName1 in compoFNames:
f2 = open(fName1)
aLine = f2.readline()
if not aLine:
gm.append("Empty file %s" % fName1)
raise Glitch, gm
#print "a got line %s" % aLine,
while aLine:
for partNum in range(self.data.nParts):
h = statsHashList[partNum]
splitLine = aLine.split()
if len(splitLine) != (self.data.nTax + 2):
gm.append("Bad line in composition stats file %s" % fName1)
gm.append("'%s'" % aLine)
raise Glitch, gm
if int(splitLine[0]) != partNum:
gm.append("Bad line in composition stats file %s" % fName1)
gm.append("First item should be the partNum %i" % partNum)
gm.append("'%s'" % aLine)
raise Glitch, gm
#for taxNum in range(self.data.nTax):
# print splitLine[taxNum + 1]
#print splitLine[-1]
h['overallSimStats'].append(float(splitLine[-1]))
for i in range(self.data.nTax):
h['individualSimStats'][i].append(float(splitLine[i + 1]))
#raise Glitch, gm
aLine = f2.readline()
if not aLine:
break
#print "b got line %s" % aLine,
f2.close()
nSims = len(statsHashList[0]['overallSimStats'])
if doOut:
flob.write(longMessage2) # Explain tree- and model-based compo fit stat, X^2_m
flob.write( ' %i simulation reps were used.\n\n' % nSims)
spacer1 = ' ' * 10
for partNum in range(self.data.nParts):
h = statsHashList[partNum]
if doOut:
flob.write('Part %-2i:\n-------\n\n' % partNum)
flob.write('Statistics from the original data\n')
flob.write('%s%30s: %f\n' % (spacer1, 'Overall observed stat', h['overallStat']))
flob.write('%s%30s:\n' % (spacer1, 'Stats for individual taxa'))
for taxNum in range(self.data.nTax):
if taxNum not in skipTaxNums[partNum]:
flob.write('%s%30s: %f\n' % (spacer1, self.data.taxNames[taxNum], h['individualStats'][taxNum]))
else:
flob.write('%s%30s: skipped\n' % (spacer1, self.data.taxNames[taxNum]))
flob.write('\nAssessment of fit from null distribution from %i simulations\n' % nSims)
flob.write('%s%30s: ' % (spacer1, 'Overall'))
prob = func.tailAreaProbability(h['overallStat'], h['overallSimStats'], verbose=0)
if doOut:
if prob <= 0.05:
flob.write('%13s' % 'Doesn\'t fit.')
else:
flob.write('%13s' % 'Fits.')
flob.write(' P = %5.3f\n' % prob)
#############
theRet= prob
#############
for taxNum in range(self.data.nTax):
if doOut: flob.write('%s%30s: ' % (spacer1, self.data.taxNames[taxNum]))
if taxNum in skipTaxNums[partNum]:
if doOut: flob.write('%13s\n' % 'skipped.')
else:
prob = func.tailAreaProbability(h['individualStats'][taxNum],
h['individualSimStats'][taxNum], verbose=0)
if doOut:
if prob <= 0.05:
flob.write('%13s' % "Doesn't fit.")
else:
flob.write('%13s' % 'Fits.')
flob.write(' P = %5.3f\n' % prob)
if writeRawStats:
fRaw.write('#\n# Tree and model based composition fit test\n')
fRaw.write('# =========================================\n')
fRaw.write('# Simulation statistics, ie the null distributions\n\n')
fRaw.write('# Part %i:\n' % partNum)
fRaw.write('part%i_overall_compo_null = %s\n' % (partNum, h['overallSimStats']))
for taxNum in range(self.data.nTax):
fRaw.write('part%i_%s_compo_null = %s\n' % (partNum,
_fixFileName(self.data.taxNames[taxNum]),
h['individualSimStats'][taxNum]))
if flob and flob != sys.stdout: # Yes, it is possible to close sys.stdout
flob.close()
if fRaw and fRaw != sys.stdout:
fRaw.close()
return theRet
