def __str__(self):
"""A human friendly representation of the analysis"""
if self == {}:
return '(no data in object)'
tt = TextTable(48)
tt.set_cols_dtype(['t', 'f'])
tt.set_cols_align(['l', 'r'])
for (k, v) in self.items():
tt.add_row([' %s'%k, v])
tt.set_deco(TextTable.HEADER)
return ''.join(['Descriptive Statistics\n ',
self.cname,
'\n==========================\n',
tt.draw()])
def __str__(self):
if self == {}:
return '(no data in object)'
tt = TextTable(max_width=0)
tt.set_cols_dtype(['t', 't'] + ['a'] * len(self.conditions_list))
tt.set_cols_align(['l', 'l'] + ['r'] * len(self.conditions_list))
tt.set_deco(TextTable.HEADER | TextTable.HLINES)
tt.header(['', ''] + sorted(self.conditions_list))
for a in sorted(self.conditions_list):
rline = [a, self.coefficient]
pline = ['', 'Sig (2-tailed)']
nline = ['', 'N']
for b in sorted(self.conditions_list):
if a == b:
rline.append('1')
pline.append(' .')
nline.append(self.N)
elif self.has_key((a, b)):
rline.append(self[(a, b)]['r'])
pline.append(self[(a, b)]['p'])
nline.append(self.N)
elif self.has_key((b, a)):
rline.append(self[(b, a)]['r'])
pline.append(self[(b, a)]['p'])
nline.append(self.N)
tt.add_row([
'%s\n%s\n%s' % (_str(r), _str(p), _str(n))
for r, p, n in zip(rline, pline, nline)
])
tt_lm = TextTable(max_width=0)
tt_lm.set_cols_dtype(['t', 'i', 'f', 'a', 'a', 't'])
tt_lm.set_cols_align(['l', 'r', 'r', 'r', 'r', 'l'])
tt_lm.set_deco(TextTable.HEADER)
tt_lm.header(
['Pair', 'i', 'Correlation', 'P', 'alpha/(k-i+1)', 'Sig.'])
for row in self.lm:
x, y = row[0]
tt_lm.add_row(['%s vs. %s' % (x, y)] + row[1:] +
([''], ['**'])[row[3] < row[4]])
return 'Bivariate Correlations\n\n' + tt.draw() + \
'\n\nLarzelere and Mulaik Significance Testing\n\n' + tt_lm.draw()
def __str__(self):
if self == {}:
return '(no data in object)'
tt_s = TextTable(max_width=0)
tt_s.set_cols_dtype(['t', 'a', 'a', 'a', 'a'])
tt_s.set_cols_align(['l', 'r', 'r', 'r', 'r'])
tt_s.set_deco(TextTable.HEADER)
tt_s.header(['Groups', 'Count', 'Sum', 'Average', 'Variance'])
for g, c, a, v in zip(self.conditions_list, self['ns'], self['mus'],
self['vars']):
tt_s.add_row([g, c, c * a, a, v])
tt_o = TextTable(max_width=0)
tt_o.set_cols_dtype(['t', 'a', 'a', 'a', 'a', 'a', 'a', 'a'])
tt_o.set_cols_align(['l', 'r', 'r', 'r', 'r', 'r', 'r', 'r'])
tt_o.set_deco(TextTable.HEADER | TextTable.FOOTER)
tt_o.header([
'Source of Variation', 'SS', 'df', 'MS', 'F', 'P-value', 'eta^2',
'Obs. power'
])
tt_o.add_row([
'Treatments', self['o_ssbn'], self['o_dfbn'], self['o_msbn'],
self['o_f'], self['o_p'], self['o_eta2'], self['o_power']
])
tt_o.add_row([
'Error', self['o_sswn'], self['o_dfwn'], self['o_mswn'], ' ', ' ',
' ', ' '
])
tt_o.footer([
'Total', self['o_ssbn'] + self['o_sswn'],
self['o_dfbn'] + self['o_dfwn'], ' ', ' ', ' ', ' ', ' '
])
tt_a = TextTable(max_width=0)
tt_a.set_cols_dtype(['t', 'a', 'a', 'a', 'a', 'a', 'a', 'a'])
tt_a.set_cols_align(['l', 'r', 'r', 'r', 'r', 'r', 'r', 'r'])
tt_a.set_deco(TextTable.HEADER | TextTable.FOOTER)
tt_a.header([
'Source of Variation', 'SS', 'df', 'MS', 'F', 'P-value', 'eta^2',
'Obs. power'
])
tt_a.add_row([
'Treatments', self['ssbn'], self['dfbn'], self['msbn'], self['f'],
self['p'], self['eta2'], self['power']
])
tt_a.add_row([
'Error', self['sswn'], self['dfwn'], self['mswn'], ' ', ' ', ' ',
' '
])
tt_a.footer([
'Total', self['ssbn'] + self['sswn'], self['dfbn'] + self['dfwn'],
' ', ' ', ' ', ' ', ' '
])
posthoc = ''
if self.posthoc.lower() == 'tukey' and self.multtest != None:
tt_m = TextTable(max_width=0)
tt_m.set_cols_dtype(['t'] + ['a'] * len(self.conditions_list))
tt_m.set_cols_align(['l'] + ['l'] * len(self.conditions_list))
tt_m.set_deco(TextTable.HEADER | TextTable.FOOTER)
tt_m.header([''] + sorted(self.conditions_list))
for a in sorted(self.conditions_list):
rline = [a]
for b in sorted(self.conditions_list):
if a == b:
rline.append('0')
elif (a, b) in self.multtest:
q = self.multtest[(a, b)]['q']
sig = self.multtest[(a, b)]['sig']
rline.append('%s %s' % (_str(q), sig))
else:
rline.append(' ')
tt_m.add_row(rline)
tt_m.footer([''] * (len(self.conditions_list) + 1))
q_crit10 = self.multtest[(a, b)]['q_crit10']
q_crit05 = self.multtest[(a, b)]['q_crit05']
q_crit01 = self.multtest[(a, b)]['q_crit01']
k = self.multtest[(a, b)]['q_k']
df = self.multtest[(a, b)]['q_df']
posthoc = 'POSTHOC MULTIPLE COMPARISONS\n\n'
posthoc += 'Tukey HSD: Table of q-statistics\n'
posthoc += tt_m.draw()
posthoc += '\n + p < .10 (q-critical[%i, %i] = %s)' % (k, df,
q_crit10)
posthoc += '\n * p < .05 (q-critical[%i, %i] = %s)' % (k, df,
q_crit05)
posthoc += '\n ** p < .01 (q-critical[%i, %i] = %s)' % (k, df,
q_crit01)
if self.posthoc.lower() == 'snk' and self.multtest != None:
tt_m = TextTable(max_width=0)
tt_m.set_cols_dtype(['t', 'i', 'f', 'a', 'a', 'a', 'a', 't'])
tt_m.set_cols_align(['l', 'r', 'r', 'r', 'r', 'r', 'r', 'l'])
tt_m.set_deco(TextTable.HEADER)
tt_m.header(
['Pair', 'i', '|diff|', 'q', 'range', 'df', 'p', 'Sig.'])
for row in self.multtest:
x, y = row[0]
tt_m.add_row(['%s vs. %s' % (x, y)] + [(v, '-')[np.isnan(v)]
for v in row[1:-1]] +
[row[-1]])
posthoc = 'POSTHOC MULTIPLE COMPARISONS\n\n'
posthoc += 'SNK: Step-down table of q-statistics\n'
posthoc += tt_m.draw()
posthoc += '\n + p < .10, * p < .05, ** p < .01, *** p < .001'
return 'Anova: Single Factor on %s\n\n'%self.val + \
'SUMMARY\n%s\n\n'%tt_s.draw() + \
"O'BRIEN TEST FOR HOMOGENEITY OF VARIANCE\n%s\n\n"%tt_o.draw() + \
'ANOVA\n%s\n\n'%tt_a.draw() + \
posthoc
def __str__(self):
tt = TextTable(48)
tt.set_cols_dtype(['f', 'f'])
tt.set_cols_align(['r', 'r'])
for (b, v) in zip(self['bin_edges'], self['values'] + ['']):
tt.add_row([b, v])
tt.set_deco(TextTable.HEADER)
tt.header(['Bins', 'Values'])
return ''.join([('', 'Cumulative ')[self.cumulative],
('', 'Density ')[self.density], 'Histogram for ',
self.cname, '\n',
tt.draw()])
def __str__(self):
"""Returns human readable string representation of ChiSquare2way"""
if self == {}:
return '(no data in object)'
# SUMMARY
tt_s = TextTable(max_width=0)
tt_s.set_cols_dtype(['t'] + ['a'] * (self.N_c + 1))
tt_s.set_cols_align(['l'] + ['r'] * (self.N_c + 1))
tt_s.set_deco(TextTable.HEADER | TextTable.FOOTER)
tt_s.header([' '] + sorted(self.col_counter) + ['Total'])
for r, rv in sorted(self.row_counter.items()):
line = [r]
for c, cv in sorted(self.col_counter.items()):
o = self.counter[(r, c)]
e = (rv * cv) / self['N']
line.append('%s\n(%s)' % (_str(o), _str(e)))
line.append(rv)
tt_s.add_row(line)
tt_s.footer(['Total'] +
[v for c, v in sorted(self.col_counter.items())] +
[self['N']])
# SYMMETRIC TESTS
tt_sym = TextTable(max_width=0)
tt_sym.set_cols_dtype(['t', 'a', 'a'])
tt_sym.set_cols_align(['l', 'r', 'r'])
tt_sym.set_deco(TextTable.HEADER)
tt_sym.header(['', 'Value', 'Approx.\nSig.'])
tt_sym.add_row(["Cramer's V", self['CramerV'], self['CramerV_prob']])
tt_sym.add_row(["Contingency Coefficient", self['C'], self['C_prob']])
tt_sym.add_row(["N of Valid Cases", self['N'], ''])
# CHI-SQUARE TESTS
tt_a = TextTable(max_width=0)
tt_a.set_cols_dtype(['t', 'a', 'a', 'a'])
tt_a.set_cols_align(['l', 'r', 'r', 'r'])
tt_a.set_deco(TextTable.HEADER)
tt_a.header([' ', 'Value', 'df', 'P'])
tt_a.add_row(
['Pearson Chi-Square', self['chisq'], self['df'], self['p']])
if self['ccchisq'] != None:
tt_a.add_row([
'Continuity Correction', self['ccchisq'], self['df'],
self['ccp']
])
tt_a.add_row(
['Likelihood Ratio', self['lnchisq'], self['df'], self['lnp']])
tt_a.add_row(["N of Valid Cases", self['N'], '', ''])
# POWER
tt_p = TextTable(max_width=0)
tt_p.set_cols_dtype(['t', 'a'])
tt_p.set_cols_align(['l', 'r'])
tt_p.set_deco(TextTable.HEADER)
tt_p.header(['Measure', ' '])
tt_p.add_row(['Effect size w', self['w']])
tt_p.add_row(['Non-centrality lambda', self['lambda']])
tt_p.add_row(['Critical Chi-Square', self['crit_chi2']])
tt_p.add_row(['Power', self['power']])
return 'Chi-Square: two Factor\n\n' + \
'SUMMARY\n%s\n\n'%tt_s.draw() + \
'SYMMETRIC MEASURES\n%s\n\n'%tt_sym.draw() + \
'CHI-SQUARE TESTS\n%s\n\n'%tt_a.draw() + \
'CHI-SQUARE POST-HOC POWER\n%s'%tt_p.draw()
def __str__(self):
if self == {}:
return '(no data in object)'
# SUMMARY
tt_s = TextTable(max_width=0)
tt_s.set_cols_dtype(['t'] + ['a']*len(self.observed))
tt_s.set_cols_align(['l'] + ['r']*len(self.observed))
tt_s.set_deco(TextTable.HEADER)
tt_s.header( [' '] + self.conditions_list)
tt_s.add_row(['Observed'] + self.observed)
tt_s.add_row(['Expected'] + self.expected)
# TESTS
tt_a = TextTable(max_width=0)
tt_a.set_cols_dtype(['t', 'a', 'a', 'a'])
tt_a.set_cols_align(['l', 'r', 'r', 'r'])
tt_a.set_deco(TextTable.HEADER)
tt_a.header([' ', 'Value', 'df', 'P'])
tt_a.add_row(['Pearson Chi-Square',
self['chisq'], self['df'], self['p']])
tt_a.add_row(['Likelihood Ratio',
self['lnchisq'], self['lndf'], self['lnp']])
tt_a.add_row(['Observations', self['N'],'',''])
# POWER
tt_p = TextTable(max_width=0)
tt_p.set_cols_dtype(['t', 'a'])
tt_p.set_cols_align(['l', 'r'])
tt_p.set_deco(TextTable.HEADER)
tt_p.header( ['Measure',' '])
tt_p.add_row(['Effect size w', self['w']])
tt_p.add_row(['Non-centrality lambda', self['lambda']])
tt_p.add_row(['Critical Chi-Square', self['crit_chi2']])
tt_p.add_row(['Power', self['power']])
return 'Chi-Square: Single Factor\n\n' + \
'SUMMARY\n%s\n\n'%tt_s.draw() + \
'CHI-SQUARE TESTS\n%s\n\n'%tt_a.draw() + \
'POST-HOC POWER\n%s'%tt_p.draw()
def __str__(self):
if self == {}:
return '(no data in object)'
if self.B == None:
tt = TextTable(max_width=100000000)
tt.set_cols_dtype(['t', 'a'])
tt.set_cols_align(['l', 'r'])
tt.set_deco(TextTable.HEADER)
first = 't-Test: One Sample for means\n'
tt.header(['', self.aname])
tt.add_row(['Sample Mean', self['mu']])
tt.add_row(['Hypothesized Pop. Mean', self['pop_mean']])
tt.add_row(['Variance', self['var']])
tt.add_row(['Observations', self['n']])
tt.add_row(['df', self['df']])
tt.add_row(['t Stat', self['t']])
tt.add_row(['alpha', self.alpha])
tt.add_row(['P(T<=t) one-tail', self['p1tail']])
tt.add_row(['t Critical one-tail', self['tc1tail']])
tt.add_row(['P(T<=t) two-tail', self['p2tail']])
tt.add_row(['t Critical two-tail', self['tc2tail']])
tt.add_row(['P(T<=t) two-tail', self['p2tail']])
tt.add_row(['Effect size d', self['cohen_d']])
tt.add_row(['delta', self['delta']])
tt.add_row(['Observed power one-tail', self['power1tail']])
tt.add_row(['Observed power two-tail', self['power2tail']])
return '%s\n%s' % (first, tt.draw())
tt = TextTable(max_width=100000000)
tt.set_cols_dtype(['t', 'a', 'a'])
tt.set_cols_align(['l', 'r', 'r'])
tt.set_deco(TextTable.HEADER)
if self.paired == True:
first = 't-Test: Paired Two Sample for means\n'
tt.header(['', self.aname, self.bname])
tt.add_row(['Mean', self['mu1'], self['mu2']])
tt.add_row(['Variance', self['var1'], self['var2']])
tt.add_row(['Observations', self['n1'], self['n2']])
tt.add_row(['Pearson Correlation', self['r'], ''])
tt.add_row(['df', self['df'], ''])
tt.add_row(['t Stat', self['t'], ''])
tt.add_row(['alpha', self.alpha, ''])
tt.add_row(['P(T<=t) one-tail', self['p1tail'], ''])
tt.add_row(['t Critical one-tail', self['tc1tail'], ''])
tt.add_row(['P(T<=t) two-tail', self['p2tail'], ''])
tt.add_row(['t Critical two-tail', self['tc2tail'], ''])
tt.add_row(['P(T<=t) two-tail', self['p2tail'], ''])
tt.add_row(['Effect size dz', self['cohen_d'], ''])
tt.add_row(['delta', self['delta'], ''])
tt.add_row(['Observed power one-tail', self['power1tail'], ''])
tt.add_row(['Observed power two-tail', self['power2tail'], ''])
elif self.equal_variance:
first = 't-Test: Two-Sample Assuming Equal Variances\n'
tt.header(['', self.aname, self.bname])
tt.add_row(['Mean', self['mu1'], self['mu2']])
tt.add_row(['Variance', self['var1'], self['var2']])
tt.add_row(['Observations', self['n1'], self['n2']])
tt.add_row(['Pooled Variance', self['vpooled'], ''])
tt.add_row(['df', self['df'], ''])
tt.add_row(['t Stat', self['t'], ''])
tt.add_row(['alpha', self.alpha, ''])
tt.add_row(['P(T<=t) one-tail', self['p1tail'], ''])
tt.add_row(['t Critical one-tail', self['tc1tail'], ''])
tt.add_row(['P(T<=t) two-tail', self['p2tail'], ''])
tt.add_row(['t Critical two-tail', self['tc2tail'], ''])
tt.add_row(['P(T<=t) two-tail', self['p2tail'], ''])
tt.add_row(['Effect size d', self['cohen_d'], ''])
tt.add_row(['delta', self['delta'], ''])
tt.add_row(['Observed power one-tail', self['power1tail'], ''])
tt.add_row(['Observed power two-tail', self['power2tail'], ''])
else:
first = 't-Test: Two-Sample Assuming Unequal Variances\n'
tt.header(['', self.aname, self.bname])
tt.add_row(['Mean', self['mu1'], self['mu2']])
tt.add_row(['Variance', self['var1'], self['var2']])
tt.add_row(['Observations', self['n1'], self['n2']])
tt.add_row(['df', self['df'], ''])
tt.add_row(['t Stat', self['t'], ''])
tt.add_row(['alpha', self.alpha, ''])
tt.add_row(['P(T<=t) one-tail', self['p1tail'], ''])
tt.add_row(['t Critical one-tail', self['tc1tail'], ''])
tt.add_row(['P(T<=t) two-tail', self['p2tail'], ''])
tt.add_row(['t Critical two-tail', self['tc2tail'], ''])
tt.add_row(['P(T<=t) two-tail', self['p2tail'], ''])
tt.add_row(['Effect size d', self['cohen_d'], ''])
tt.add_row(['delta', self['delta'], ''])
tt.add_row(['Observed power one-tail', self['power1tail'], ''])
tt.add_row(['Observed power two-tail', self['power2tail'], ''])
return ''.join([first, tt.draw()])
def __str__(self):
if self == {}:
return '(no data in object)'
tt_s = TextTable(max_width=0)
tt_s.set_cols_dtype(['t', 'a', 'a', 'a', 'a'])
tt_s.set_cols_align(['l', 'r', 'r', 'r', 'r'])
tt_s.set_deco(TextTable.HEADER)
tt_s.header( ['Groups','Count','Sum', 'Average','Variance'])
for g, c, a, v in zip(self.conditions_list,
self['ns'],
self['mus'],
self['vars']):
tt_s.add_row([g, c, c * a, a, v])
tt_o = TextTable(max_width=0)
tt_o.set_cols_dtype(['t', 'a', 'a', 'a', 'a', 'a', 'a', 'a'])
tt_o.set_cols_align(['l', 'r', 'r', 'r', 'r', 'r', 'r', 'r'])
tt_o.set_deco(TextTable.HEADER | TextTable.FOOTER)
tt_o.header( ['Source of Variation','SS','df','MS','F','P-value','eta^2','Obs. power'])
tt_o.add_row(['Treatments',self['o_ssbn'],self['o_dfbn'],
self['o_msbn'],self['o_f'],self['o_p'],
self['o_eta2'],self['o_power']])
tt_o.add_row(['Error', self['o_sswn'],self['o_dfwn'],
self['o_mswn'],' ', ' ',' ', ' '])
tt_o.footer( ['Total',self['o_ssbn']+self['o_sswn'],
self['o_dfbn']+self['o_dfwn'],' ',' ',' ',' ', ' '])
tt_a = TextTable(max_width=0)
tt_a.set_cols_dtype(['t', 'a', 'a', 'a', 'a', 'a', 'a', 'a'])
tt_a.set_cols_align(['l', 'r', 'r', 'r', 'r', 'r', 'r', 'r'])
tt_a.set_deco(TextTable.HEADER | TextTable.FOOTER)
tt_a.header( ['Source of Variation','SS','df','MS','F','P-value','eta^2','Obs. power'])
tt_a.add_row(['Treatments',self['ssbn'],self['dfbn'],
self['msbn'],self['f'],self['p'],
self['eta2'],self['power']])
tt_a.add_row(['Error', self['sswn'],self['dfwn'],
self['mswn'],' ', ' ',' ', ' '])
tt_a.footer( ['Total',self['ssbn']+self['sswn'],
self['dfbn']+self['dfwn'],' ',' ',' ',' ', ' '])
posthoc = ''
if self.posthoc.lower() == 'tukey' and self.multtest != None:
tt_m = TextTable(max_width=0)
tt_m.set_cols_dtype(['t'] + ['a']*len(self.conditions_list))
tt_m.set_cols_align(['l'] + ['l']*len(self.conditions_list))
tt_m.set_deco(TextTable.HEADER | TextTable.FOOTER)
tt_m.header([''] + sorted(self.conditions_list))
for a in sorted(self.conditions_list):
rline = [a]
for b in sorted(self.conditions_list):
if a == b:
rline.append('0')
elif self.multtest.has_key((a,b)):
q = self.multtest[(a,b)]['q']
sig = self.multtest[(a,b)]['sig']
rline.append('%s %s'%(_str(q), sig))
else:
rline.append(' ')
tt_m.add_row(rline)
tt_m.footer(['']*(len(self.conditions_list) + 1))
q_crit10 = self.multtest[(a,b)]['q_crit10']
q_crit05 = self.multtest[(a,b)]['q_crit05']
q_crit01 = self.multtest[(a,b)]['q_crit01']
k = self.multtest[(a,b)]['q_k']
df = self.multtest[(a,b)]['q_df']
posthoc = 'POSTHOC MULTIPLE COMPARISONS\n\n'
posthoc += 'Tukey HSD: Table of q-statistics\n'
posthoc += tt_m.draw()
posthoc += '\n + p < .10 (q-critical[%i, %i] = %s)'%(k, df, q_crit10)
posthoc += '\n * p < .05 (q-critical[%i, %i] = %s)'%(k, df, q_crit05)
posthoc += '\n ** p < .01 (q-critical[%i, %i] = %s)'%(k, df, q_crit01)
if self.posthoc.lower() == 'snk' and self.multtest != None:
tt_m = TextTable(max_width=0)
tt_m.set_cols_dtype(['t', 'i', 'f', 'a', 'a', 'a', 'a', 't'])
tt_m.set_cols_align(['l', 'r', 'r', 'r', 'r', 'r', 'r', 'l'])
tt_m.set_deco(TextTable.HEADER)
tt_m.header(['Pair', 'i', '|diff|', 'q', 'range', 'df', 'p', 'Sig.'])
for row in self.multtest:
x, y = row[0]
tt_m.add_row(['%s vs. %s'%(x, y)] +
[(v,'-')[np.isnan(v)] for v in row[1:-1]] +
[row[-1]])
posthoc = 'POSTHOC MULTIPLE COMPARISONS\n\n'
posthoc += 'SNK: Step-down table of q-statistics\n'
posthoc += tt_m.draw()
posthoc += '\n + p < .10, * p < .05, ** p < .01, *** p < .001'
return 'Anova: Single Factor on %s\n\n'%self.val + \
'SUMMARY\n%s\n\n'%tt_s.draw() + \
"O'BRIEN TEST FOR HOMOGENEITY OF VARIANCE\n%s\n\n"%tt_o.draw() + \
'ANOVA\n%s\n\n'%tt_a.draw() + \
posthoc
def __str__(self):
"""Returns human readable string representaition of Marginals"""
M = []
for v in self['factorials'].values():
M.append(v)
M.append(self['dmu'])
M.append(self['dN'])
M.append(self['dsem'])
M.append(self['dlower'])
M.append(self['dupper'])
M = zip(*M) # transpose
# figure out the width needed by the condition labels so we can
# set the width of the table
flength = sum([max([len(v) for c in v])
for v in self['factorials'].values()])
flength += len(self['factorials']) * 2
# build the header
header = self.factors + 'Mean;Count;Std.\nError;'\
'95% CI\nlower;95% CI\nupper'.split(';')
dtypes = ['t'] * len(self.factors) + ['f', 'i', 'f', 'f', 'f']
aligns = ['l'] * len(self.factors) + ['r', 'l', 'r', 'r', 'r']
# initialize the texttable and add stuff
tt = TextTable(max_width=10000000)
tt.set_cols_dtype(dtypes)
tt.set_cols_align(aligns)
tt.add_rows(M, header=False)
tt.header(header)
tt.set_deco(TextTable.HEADER)
# output the table
return tt.draw()
def __str__(self):
tt = TextTable(48)
tt.set_cols_dtype(['f', 'f'])
tt.set_cols_align(['r', 'r'])
for (b, v) in zip(self['bin_edges'],self['values']+['']):
tt.add_row([b, v])
tt.set_deco(TextTable.HEADER)
tt.header(['Bins','Values'])
return ''.join([('','Cumulative ')[self.cumulative],
('','Density ')[self.density],
'Histogram for ', self.cname, '\n',
tt.draw()])
def __str__(self):
if self == {}:
return '(no data in object)'
if self.B == None:
tt = TextTable(max_width=100000000)
tt.set_cols_dtype(['t', 'a'])
tt.set_cols_align(['l', 'r'])
tt.set_deco(TextTable.HEADER)
first = 't-Test: One Sample for means\n'
tt.header( ['', self.aname])
tt.add_row(['Sample Mean', self['mu']])
tt.add_row(['Hypothesized Pop. Mean', self['pop_mean']])
tt.add_row(['Variance', self['var']])
tt.add_row(['Observations', self['n']])
tt.add_row(['df', self['df']])
tt.add_row(['t Stat', self['t']])
tt.add_row(['alpha', self.alpha])
tt.add_row(['P(T<=t) one-tail', self['p1tail']])
tt.add_row(['t Critical one-tail', self['tc1tail']])
tt.add_row(['P(T<=t) two-tail', self['p2tail']])
tt.add_row(['t Critical two-tail', self['tc2tail']])
tt.add_row(['P(T<=t) two-tail', self['p2tail']])
tt.add_row(['Effect size d', self['cohen_d']])
tt.add_row(['delta', self['delta']])
tt.add_row(['Observed power one-tail', self['power1tail']])
tt.add_row(['Observed power two-tail', self['power2tail']])
return '%s\n%s'%(first, tt.draw())
tt = TextTable(max_width=100000000)
tt.set_cols_dtype(['t', 'a', 'a'])
tt.set_cols_align(['l', 'r', 'r'])
tt.set_deco(TextTable.HEADER)
if self.paired == True:
first = 't-Test: Paired Two Sample for means\n'
tt.header( ['', self.aname, self.bname])
tt.add_row(['Mean', self['mu1'], self['mu2']])
tt.add_row(['Variance', self['var1'], self['var2']])
tt.add_row(['Observations', self['n1'], self['n2']])
tt.add_row(['Pearson Correlation', self['r'], ''])
tt.add_row(['df', self['df'], ''])
tt.add_row(['t Stat', self['t'], ''])
tt.add_row(['alpha', self.alpha, ''])
tt.add_row(['P(T<=t) one-tail', self['p1tail'], ''])
tt.add_row(['t Critical one-tail', self['tc1tail'], ''])
tt.add_row(['P(T<=t) two-tail', self['p2tail'], ''])
tt.add_row(['t Critical two-tail', self['tc2tail'], ''])
tt.add_row(['P(T<=t) two-tail', self['p2tail'], ''])
tt.add_row(['Effect size dz', self['cohen_d'], ''])
tt.add_row(['delta', self['delta'], ''])
tt.add_row(['Observed power one-tail', self['power1tail'], ''])
tt.add_row(['Observed power two-tail', self['power2tail'], ''])
elif self.equal_variance:
first = 't-Test: Two-Sample Assuming Equal Variances\n'
tt.header( ['', self.aname, self.bname])
tt.add_row(['Mean', self['mu1'], self['mu2']])
tt.add_row(['Variance', self['var1'], self['var2']])
tt.add_row(['Observations', self['n1'], self['n2']])
tt.add_row(['Pooled Variance', self['vpooled'], ''])
tt.add_row(['df', self['df'], ''])
tt.add_row(['t Stat', self['t'], ''])
tt.add_row(['alpha', self.alpha, ''])
tt.add_row(['P(T<=t) one-tail', self['p1tail'], ''])
tt.add_row(['t Critical one-tail', self['tc1tail'], ''])
tt.add_row(['P(T<=t) two-tail', self['p2tail'], ''])
tt.add_row(['t Critical two-tail', self['tc2tail'], ''])
tt.add_row(['P(T<=t) two-tail', self['p2tail'], ''])
tt.add_row(['Effect size d', self['cohen_d'], ''])
tt.add_row(['delta', self['delta'], ''])
tt.add_row(['Observed power one-tail', self['power1tail'], ''])
tt.add_row(['Observed power two-tail', self['power2tail'], ''])
else:
first = 't-Test: Two-Sample Assuming Unequal Variances\n'
tt.header( ['', self.aname, self.bname])
tt.add_row(['Mean', self['mu1'], self['mu2']])
tt.add_row(['Variance', self['var1'], self['var2']])
tt.add_row(['Observations', self['n1'], self['n2']])
tt.add_row(['df', self['df'], ''])
tt.add_row(['t Stat', self['t'], ''])
tt.add_row(['alpha', self.alpha, ''])
tt.add_row(['P(T<=t) one-tail', self['p1tail'], ''])
tt.add_row(['t Critical one-tail', self['tc1tail'], ''])
tt.add_row(['P(T<=t) two-tail', self['p2tail'], ''])
tt.add_row(['t Critical two-tail', self['tc2tail'], ''])
tt.add_row(['P(T<=t) two-tail', self['p2tail'], ''])
tt.add_row(['Effect size d', self['cohen_d'], ''])
tt.add_row(['delta', self['delta'], ''])
tt.add_row(['Observed power one-tail', self['power1tail'], ''])
tt.add_row(['Observed power two-tail', self['power2tail'], ''])
return ''.join([first,tt.draw()])
def __str__(self):
"""Returns human readable string representation of ChiSquare2way"""
if self == {}:
return '(no data in object)'
# SUMMARY
tt_s = TextTable(max_width=0)
tt_s.set_cols_dtype(['t'] + ['a']*(self.N_c + 1))
tt_s.set_cols_align(['l'] + ['r']*(self.N_c + 1))
tt_s.set_deco(TextTable.HEADER | TextTable.FOOTER)
tt_s.header( [' '] + sorted(self.col_counter) + ['Total'])
for r, rv in sorted(self.row_counter.items()):
line = [r]
for c, cv in sorted(self.col_counter.items()):
o = self.counter[(r,c)]
e = (rv*cv)/self['N']
line.append('%s\n(%s)'%(_str(o), _str(e)))
line.append(rv)
tt_s.add_row(line)
tt_s.footer(['Total'] +
[v for c,v in sorted(self.col_counter.items())] +
[self['N']])
# SYMMETRIC TESTS
tt_sym = TextTable(max_width=0)
tt_sym.set_cols_dtype(['t', 'a', 'a'])
tt_sym.set_cols_align(['l', 'r', 'r'])
tt_sym.set_deco(TextTable.HEADER)
tt_sym.header(['','Value','Approx.\nSig.'])
tt_sym.add_row(["Cramer's V", self['CramerV'], self['CramerV_prob']])
tt_sym.add_row(["Contingency Coefficient", self['C'], self['C_prob']])
tt_sym.add_row(["N of Valid Cases", self['N'], ''])
# CHI-SQUARE TESTS
tt_a = TextTable(max_width=0)
tt_a.set_cols_dtype(['t', 'a', 'a', 'a'])
tt_a.set_cols_align(['l', 'r', 'r', 'r'])
tt_a.set_deco(TextTable.HEADER)
tt_a.header([' ', 'Value', 'df', 'P'])
tt_a.add_row(['Pearson Chi-Square',
self['chisq'], self['df'], self['p']])
if self['ccchisq'] != None:
tt_a.add_row(['Continuity Correction',
self['ccchisq'], self['df'], self['ccp']])
tt_a.add_row(['Likelihood Ratio',
self['lnchisq'], self['df'], self['lnp']])
tt_a.add_row(["N of Valid Cases", self['N'], '', ''])
# POWER
tt_p = TextTable(max_width=0)
tt_p.set_cols_dtype(['t', 'a'])
tt_p.set_cols_align(['l', 'r'])
tt_p.set_deco(TextTable.HEADER)
tt_p.header( ['Measure',' '])
tt_p.add_row(['Effect size w', self['w']])
tt_p.add_row(['Non-centrality lambda', self['lambda']])
tt_p.add_row(['Critical Chi-Square', self['crit_chi2']])
tt_p.add_row(['Power', self['power']])
return 'Chi-Square: two Factor\n\n' + \
'SUMMARY\n%s\n\n'%tt_s.draw() + \
'SYMMETRIC MEASURES\n%s\n\n'%tt_sym.draw() + \
'CHI-SQUARE TESTS\n%s\n\n'%tt_a.draw() + \
'CHI-SQUARE POST-HOC POWER\n%s'%tt_p.draw()
def __str__(self):
if self == {}:
return '(no data in object)'
tt = TextTable(max_width=0)
tt.set_cols_dtype(['t', 't'] + ['a']*len(self.conditions_list))
tt.set_cols_align(['l', 'l'] + ['r']*len(self.conditions_list))
tt.set_deco(TextTable.HEADER | TextTable.HLINES)
tt.header(['',''] + sorted(self.conditions_list))
for a in sorted(self.conditions_list):
rline = [a, self.coefficient]
pline = ['', 'Sig (2-tailed)']
nline = ['', 'N']
for b in sorted(self.conditions_list):
if a == b:
rline.append('1')
pline.append(' .')
nline.append(self.N)
elif self.has_key((a,b)):
rline.append(self[(a,b)]['r'])
pline.append(self[(a,b)]['p'])
nline.append(self.N)
elif self.has_key((b,a)):
rline.append(self[(b,a)]['r'])
pline.append(self[(b,a)]['p'])
nline.append(self.N)
tt.add_row(['%s\n%s\n%s'%(_str(r),_str(p),_str(n))
for r,p,n in zip(rline,pline,nline)])
tt_lm = TextTable(max_width=0)
tt_lm.set_cols_dtype(['t', 'i', 'f', 'a', 'a', 't'])
tt_lm.set_cols_align(['l', 'r', 'r', 'r', 'r', 'l'])
tt_lm.set_deco(TextTable.HEADER)
tt_lm.header(['Pair', 'i', 'Correlation', 'P', 'alpha/(k-i+1)', 'Sig.'])
for row in self.lm:
x, y = row[0]
tt_lm.add_row(['%s vs. %s'%(x, y)] +
row[1:] +
([''],['**'])[row[3] < row[4]])
return 'Bivariate Correlations\n\n' + tt.draw() + \
'\n\nLarzelere and Mulaik Significance Testing\n\n' + tt_lm.draw()