def compare(Y, first_model, test_effect, sub=None):
"""
OBSOLETE
Tests for a significant influence of test_effect by comparing whether
(first_model + test_effect) explains significantly more variance than
first_model alone.
"""
a1 = lm(Y, first_model, sub=sub)
a2 = lm(Y, first_model + test_effect, sub=sub)
print
print a1.anova(title="MODEL 1:")
print "\n"
print a2.anova(title="MODEL 2:")
# compare
SS_diff = a1.SS_res - a2.SS_res
df_diff = test_effect.df
MS_diff = SS_diff / df_diff
# if not round(SS_diff, 6) == round(SS_cov_1 - SS_cov_2, 6):
# txt = "\nWARNING: SS_diff: {0} a1.SS_res - a2.SS_res: {1}"
# print txt.format(SS_diff, a1.SS_res - a2.SS_res)
F = MS_diff / a2.MS_res
p = 1 - sp.stats.distributions.f.cdf(F, df_diff, a2.df_res)
stars = test.star(p).replace(" ", "")
difftxt = "Residual SS reduction: {SS}, df difference: {df}, " + "F = {F}{s}, p = {p}"
print "\n" + difftxt.format(SS=SS_diff, df=df_diff, F=F, s=stars, p=p)
def anova(self):
"Return an ANOVA table"
# table head
table = fmtxt.Table("l" + "r" * 5)
if self.title:
table.title(self.title)
table.cell()
headers = ["SS", "df", "MS"]
headers += ["F", "p"]
for hd in headers:
table.cell(hd, r"\textbf", just="c")
table.midrule()
# table body
for name, F_test in zip(self.names, self.F_tests):
table.cell(name)
table.cell(fmtxt.stat(F_test.SS))
table.cell(fmtxt.stat(F_test.df, fmt="%i"))
table.cell(fmtxt.stat(F_test.MS))
if F_test.F:
stars = test.star(F_test.p)
table.cell(fmtxt.stat(F_test.F, stars=stars))
table.cell(fmtxt.p(F_test.p))
else:
table.cell()
table.cell()
# residuals
if self.X.df_error > 0:
table.empty_row()
table.cell("Residuals")
SS, df, MS = self.residuals
table.cell(SS)
table.cell(df, fmt="%i")
table.cell(MS)
table.endline()
# total
table.midrule()
table.cell("Total")
SS = np.sum((self.Y.x - self.Y.mean()) ** 2)
table.cell(fmtxt.stat(SS))
table.cell(fmtxt.stat(len(self.Y) - 1, fmt="%i"))
return table
def comparelm(lm1, lm2):
"""
Fox (p. 109)
"""
if lm2.df_res > lm1.df_res:
mtemp = lm1
lm1 = lm2
lm2 = mtemp
else:
assert lm1.df_res != lm2.df_res
SS_diff = lm1.SS_res - lm2.SS_res
df_diff = lm1.df_res - lm2.df_res
MS_diff = SS_diff / df_diff
F = MS_diff / lm2.MS_res
p = 1 - sp.stats.distributions.f.cdf(F, df_diff, lm2.df_res)
stars = test.star(p).replace(" ", "")
difftxt = "Residual SS reduction: {SS}, df difference: {df}, " + "F = {F:.3f}{s}, p = {p:.4f}"
return difftxt.format(SS=SS_diff, df=df_diff, F=F, s=stars, p=p)
def anova(self):
"Return ANOVA table"
if self.show_ems is None:
ems = defaults["show_ems"]
else:
ems = self.show_ems
# table head
table = textab.Table("l" + "r" * (5 + ems))
if self.title:
table.title(self.title)
table.cell()
headers = ["SS", "df", "MS"]
# if ems: headers += ["E(MS)"]
headers += ["F", "p"]
for hd in headers:
table.cell(hd, r"\textbf", just="c")
table.midrule()
# table body
for name, SS, df, MS, F, p in self._results_table:
table.cell(name)
table.cell(textab.stat(SS))
table.cell(textab.stat(df, fmt="%i"))
table.cell(textab.stat(MS))
if F:
stars = test.star(p)
table.cell(textab.stat(F, stars=stars))
table.cell(textab.p(p))
else:
table.cell()
table.cell()
# total
table.midrule()
table.cell("Total")
table.cell(textab.stat(self.Y.SS))
table.cell(textab.stat(self.Y.N - 1, fmt="%i"))
return table
def anova(self, title=None, empty=True, ems=None):
"""
returns an ANOVA table for the linear model
"""
if ems is None:
ems = defaults["show_ems"]
Y = self.Y
X = self.X
values = self.values
# method
# if X.df_error == 0:
# hopkins = True
e_ms = _hopkins_ems(X)
# else:
# hopkins = False
# table head
table = textab.Table("l" + "r" * (5 + ems))
if title:
table.title(title)
elif self.title:
table.title(self.title)
# for msg in X.check():
# table.caption('! '+msg)
table.cell()
headers = ["SS", "df", "MS"]
if ems:
headers += ["E(MS)"]
headers += ["F", "p"]
for hd in headers:
table.cell(hd, r"\textbf", just="c")
table.midrule()
if isbalanced(X):
# MS for factors (Needed for models involving random effects)
self.MS = []
for i, name, index, df in X.iter_effects():
SS = np.sum(values[:, index].sum(1) ** 2)
self.MS.append(SS / df)
else:
raise NotImplementedError()
tests = {}
for e in X.effects: # effect to test
m0effects = []
for e0 in X.effects: # effect in model0
if e0 is e:
pass
elif all([f in e0.factors for f in e.factors]):
pass
else:
m0effects.append(e0)
model0 = model(m0effects)
model1 = model0 + e
SS, df, MS, F, p = incremental_F_test(Y, model1, model0)
tests[e.name] = dict(SS=SS, df=df, MS=MS, F=F, p=p)
# table body
self.results = {}
for i, name, index, df in X.iter_effects():
SS = np.sum(values[:, index].sum(1) ** 2)
# if v: print name, index, SS
MS = SS / df
# self.results[name] = {'SS':SS, 'df':df, 'MS':MS}
if e_ms[i] != None: # hopkins and
e_ms_i = e_ms[i]
MS_d = self.MS[e_ms_i]
df_d = X.effects[e_ms_i].df
e_ms_name = X.effects[e_ms_i].name
elif self.df_res > 0:
df_d = self.df_res
MS_d = self.MS_res
e_ms_name = "Res"
else:
MS_d = False
e_ms_name = None
# F-test
if MS_d != False:
F = MS / MS_d
p = 1 - sp.stats.distributions.f.cdf(F, df, df_d)
stars = test.star(p)
tex_stars = textab.Stars(stars)
F_tex = [F, tex_stars]
else:
F_tex = None
p = None
# add to table
if e_ms_name or empty:
table.cell(name)
table.cell(SS)
table.cell(df, fmt="%i")
table.cell(MS)
if ems:
table.cell(e_ms_name)
table.cell(F_tex, mat=True)
table.cell(p, fmt=defaults["p_fmt"], drop0=True)
# store results
self.results[name] = {"SS": SS, "df": df, "MS": MS, "E(MS)": e_ms_name, "F": F, "p": p}
# self.indexes[name] = index # for self.Ysub()
# table end
if self.df_res > 0:
table.cell("Residuals")
table.cell(self.SS_res)
table.cell(self.df_res, fmt="%i")
table.cell(self.MS_res)
return table
def anova(self, title="ANOVA", empty=True, ems=False):
"""
returns an ANOVA table for the linear model
"""
X = self.X
values = self.beta * self.X.full
if X.df_error == 0:
e_ms = hopkins_ems(X)
elif hasrandom(X):
err = "Models containing random effects need to be fully " "specified."
raise NotImplementedError(err)
else:
e_ms = False
# table head
table = fmtxt.Table("l" + "r" * (5 + ems))
if title:
table.title(title)
if not isbalanced(X):
table.caption("Warning: model is unbalanced, use anova class")
table.cell()
headers = ["SS", "df", "MS"]
if ems:
headers += ["E(MS)"]
headers += ["F", "p"]
for hd in headers:
table.cell(hd, r"\textbf", just="c")
table.midrule()
# MS for factors (Needed for models involving random effects)
MSs = {}
SSs = {}
for e in X.effects:
idx = X.full_index[e]
SSs[e] = SS = np.sum(values[:, idx].sum(1) ** 2)
MSs[e] = SS / e.df
# table body
results = {}
for e in X.effects:
MS = MSs[e]
if e_ms:
e_EMS = e_ms[e]
df_d = sum(c.df for c in e_EMS)
MS_d = sum(MSs[c] for c in e_EMS)
e_ms_name = " + ".join(repr(c) for c in e_EMS)
else:
df_d = self.df_res
MS_d = self.MS_res
e_ms_name = "Res"
# F-test
if MS_d != False:
F = MS / MS_d
p = 1 - scipy.stats.distributions.f.cdf(F, e.df, df_d)
stars = test.star(p)
tex_stars = fmtxt.Stars(stars)
F_tex = [F, tex_stars]
else:
F_tex = None
p = None
# add to table
if e_ms_name or empty:
table.cell(e.name)
table.cell(SSs[e])
table.cell(e.df, fmt="%i")
table.cell(MS)
if ems:
table.cell(e_ms_name)
table.cell(F_tex, mat=True)
table.cell(fmtxt.p(p))
# store results
results[e.name] = {"SS": SS, "df": e.df, "MS": MS, "E(MS)": e_ms_name, "F": F, "p": p}
# Residuals
if self.df_res > 0:
table.cell("Residuals")
table.cell(self.SS_res)
table.cell(self.df_res, fmt="%i")
table.cell(self.MS_res)
return table
