class ANOVA3onerm(ANOVA3rm):
'''Only C is an RM factor.'''
def __init__(self, A, B, C, SUBJ):
self.X = None
self.A = Factor(A)
self.B = Factor(B)
self.C = Factor(C)
self.S = FactorNestedTwoWay(SUBJ, self.A, self.B)
# self.S = FactorNestedTwoWay(SUBJ, self.B, self.C)
self.J = self.A.J
self.contrasts = None
self.term_labels = [
'Intercept', 'A', 'B', 'C', 'AB', 'AC', 'BC', 'ABC', 'S', 'SC'
]
self.f_terms = [('A', 'S'), ('B', 'S'), ('C', 'SC'), ('AB', 'S'),
('AC', 'SC'), ('BC', 'SC'), ('ABC', 'SC')]
self._check_balanced()
self._assemble()
def _assemble(self):
### assemble design matrix columns:
XCONST = self._get_column_const()
XA = self.A.get_design_main()
XB = self.B.get_design_main()
XC = self.C.get_design_main()
XS = self.S.get_design_main()
XAB = self.A.get_design_interaction(self.B)
XAC = self.A.get_design_interaction(self.C)
XBC = self.B.get_design_interaction(self.C)
XABC = self.C.get_design_interaction_3way(self.A, self.B)
XSC = self.S.get_design_interaction(self.C)
### specify builder and add design matrix columns:
builder = DesignBuilder(labels=self.term_labels)
builder.add_main_columns('Intercept', XCONST)
builder.add_main_columns('A', XA)
builder.add_main_columns('B', XB)
builder.add_main_columns('C', XC)
builder.add_main_columns('AB', XAB)
builder.add_main_columns('AC', XAC)
builder.add_main_columns('BC', XBC)
builder.add_main_columns('ABC', XABC)
builder.add_main_columns('S', XS)
builder.add_main_columns('SC', XSC)
### assemble design matrix and contrasts:
self.X = builder.get_design_matrix()
self.contrasts = builder.get_contrasts()
def _check_balanced(self):
if not (self.A.balanced and self.B.balanced and self.C.balanced
and self.S.balanced):
raise (ValueError('Design must be balanced.'))
if not self.A.check_balanced(self.B):
raise (ValueError('Design must be balanced.'))
if not self.A.check_balanced(self.C):
raise (ValueError('Design must be balanced.'))
if not self.B.check_balanced(self.C):
raise (ValueError('Design must be balanced.'))
if not self.S.check_balanced_rm(self.C):
raise (ValueError('Design must be balanced.'))
class ANOVA3onerm(ANOVA3rm):
'''Only C is an RM factor.'''
def __init__(self, A, B, C, SUBJ):
self.X = None
self.A = Factor(A)
self.B = Factor(B)
self.C = Factor(C)
self.S = FactorNestedTwoWay(SUBJ, self.A, self.B)
# self.S = FactorNestedTwoWay(SUBJ, self.B, self.C)
self.J = self.A.J
self.contrasts = None
self.term_labels = ['Intercept', 'A','B','C', 'AB','AC','BC', 'ABC', 'S', 'SC']
self.f_terms = [('A','S'), ('B','S'), ('C','SC'), ('AB','S'), ('AC','SC'), ('BC','SC'), ('ABC','SC')]
self._check_balanced()
self._assemble()
def _assemble(self):
### assemble design matrix columns:
XCONST = self._get_column_const()
XA = self.A.get_design_main()
XB = self.B.get_design_main()
XC = self.C.get_design_main()
XS = self.S.get_design_main()
XAB = self.A.get_design_interaction(self.B)
XAC = self.A.get_design_interaction(self.C)
XBC = self.B.get_design_interaction(self.C)
XABC = self.C.get_design_interaction_3way(self.A, self.B)
XSC = self.S.get_design_interaction(self.C)
### specify builder and add design matrix columns:
builder = DesignBuilder(labels=self.term_labels)
builder.add_main_columns('Intercept', XCONST)
builder.add_main_columns('A', XA)
builder.add_main_columns('B', XB)
builder.add_main_columns('C', XC)
builder.add_main_columns('AB', XAB)
builder.add_main_columns('AC', XAC)
builder.add_main_columns('BC', XBC)
builder.add_main_columns('ABC', XABC)
builder.add_main_columns('S', XS)
builder.add_main_columns('SC', XSC)
### assemble design matrix and contrasts:
self.X = builder.get_design_matrix()
self.contrasts = builder.get_contrasts()
def _check_balanced(self):
if not (self.A.balanced and self.B.balanced and self.C.balanced and self.S.balanced):
raise( ValueError('Design must be balanced.') )
if not self.A.check_balanced(self.B):
raise( ValueError('Design must be balanced.') )
if not self.A.check_balanced(self.C):
raise( ValueError('Design must be balanced.') )
if not self.B.check_balanced(self.C):
raise( ValueError('Design must be balanced.') )
if not self.S.check_balanced_rm(self.C):
raise( ValueError('Design must be balanced.') )
class ANOVA3(_Design):
def __init__(self, A, B, C):
self.X = None #design matrix
self.A = Factor(A) #factor level vector
self.B = Factor(B) #factor level vector
self.C = Factor(C) #factor level vector
self.J = self.A.J #number of observations
self.term_labels = [
'Intercept', 'A', 'B', 'C', 'AB', 'AC', 'BC', 'ABC'
]
self.f_terms = [('A', 'Error'), ('B', 'Error'), ('C', 'Error'),
('AB', 'Error'), ('AC', 'Error'), ('BC', 'Error'),
('ABC', 'Error')]
self.contrasts = None
self._check_balanced()
self._assemble()
def _assemble(self):
### assemble design matrix columns:
XCONST = self._get_column_const()
XA = self.A.get_design_main()
XB = self.B.get_design_main()
XC = self.C.get_design_main()
XAB = self.A.get_design_interaction(self.B)
XAC = self.A.get_design_interaction(self.C)
XBC = self.B.get_design_interaction(self.C)
XABC = self.A.get_design_interaction_3way(self.B, self.C)
### specify builder and add design matrix columns:
builder = DesignBuilder(
labels=['Intercept', 'A', 'B', 'C', 'AB', 'AC', 'BC', 'ABC'])
builder.add_main_columns('Intercept', XCONST)
builder.add_main_columns('A', XA)
builder.add_main_columns('B', XB)
builder.add_main_columns('C', XC)
builder.add_main_columns('AB', XAB)
builder.add_main_columns('AC', XAC)
builder.add_main_columns('BC', XBC)
builder.add_main_columns('ABC', XABC)
### assemble design matrix and contrasts:
self.X = builder.get_design_matrix()
self.contrasts = builder.get_contrasts()
def _check_balanced(self):
if not (self.A.balanced and self.B.balanced and self.C.balanced):
raise (ValueError('Design must be balanced.'))
if not self.A.check_balanced(self.B):
raise (ValueError('Design must be balanced.'))
if not self.A.check_balanced(self.C):
raise (ValueError('Design must be balanced.'))
if not self.B.check_balanced(self.C):
raise (ValueError('Design must be balanced.'))
class ANOVA3(_Design):
def __init__(self, A, B, C):
self.X = None #design matrix
self.A = Factor(A) #factor level vector
self.B = Factor(B) #factor level vector
self.C = Factor(C) #factor level vector
self.J = self.A.J #number of observations
self.term_labels = ['Intercept', 'A', 'B', 'C', 'AB', 'AC', 'BC', 'ABC']
self.f_terms = [('A','Error'), ('B','Error'), ('C','Error'), ('AB','Error'), ('AC','Error'), ('BC','Error'), ('ABC','Error')]
self.contrasts = None
self._check_balanced()
self._assemble()
def _assemble(self):
### assemble design matrix columns:
XCONST = self._get_column_const()
XA = self.A.get_design_main()
XB = self.B.get_design_main()
XC = self.C.get_design_main()
XAB = self.A.get_design_interaction(self.B)
XAC = self.A.get_design_interaction(self.C)
XBC = self.B.get_design_interaction(self.C)
XABC = self.A.get_design_interaction_3way(self.B, self.C)
### specify builder and add design matrix columns:
builder = DesignBuilder(labels=['Intercept', 'A', 'B', 'C', 'AB', 'AC', 'BC', 'ABC'])
builder.add_main_columns('Intercept', XCONST)
builder.add_main_columns('A', XA)
builder.add_main_columns('B', XB)
builder.add_main_columns('C', XC)
builder.add_main_columns('AB', XAB)
builder.add_main_columns('AC', XAC)
builder.add_main_columns('BC', XBC)
builder.add_main_columns('ABC', XABC)
### assemble design matrix and contrasts:
self.X = builder.get_design_matrix()
self.contrasts = builder.get_contrasts()
def _check_balanced(self):
if not (self.A.balanced and self.B.balanced and self.C.balanced):
raise( ValueError('Design must be balanced.') )
if not self.A.check_balanced(self.B):
raise( ValueError('Design must be balanced.') )
if not self.A.check_balanced(self.C):
raise( ValueError('Design must be balanced.') )
if not self.B.check_balanced(self.C):
raise( ValueError('Design must be balanced.') )
class ANOVA1rm(_Design):
def __init__(self, A, SUBJ):
self.X = None #design matrix
self.S = Factor(SUBJ) #subjects
self.A = Factor(A) #factor levels
self.J = self.A.J #number of observations
self.contrasts = None #contrast matrix
self.term_labels = ['Intercept', 'A', 'S', 'SA']
self.f_terms = [('A', 'SA')]
self._check_balanced()
self._assemble()
def _assemble(self):
### assemble design matrix columns:
XCONST = self._get_column_const()
XA = self.A.get_design_main()
XS = self.S.get_design_main()
XSA = self.A.get_design_interaction(self.S)
### specify builder and add design matrix columns:
builder = DesignBuilder(labels=self.term_labels)
builder.add_main_columns('Intercept', XCONST)
builder.add_main_columns('A', XA)
builder.add_main_columns('S', XS)
builder.add_main_columns('SA', XSA)
### assemble design matrix and contrasts:
self.X = builder.get_design_matrix()
self.contrasts = builder.get_contrasts()
def _check_balanced(self):
if not (self.A.balanced and self.S.balanced):
raise (ValueError('Design must be balanced.'))
if not self.S.check_balanced(self.A):
raise (ValueError('Design must be balanced.'))
def check_for_single_responses(self):
A, S = self.A.A, self.S.A
only_single = False
for a in self.A.u:
s = S[(A == a)]
if np.unique(s).size == s.size:
only_single = True
warnings.warn(
'\nWARNING: Only one observation per subject found. Residuals and inference will be approximate. To avoid approximate residuals: (a) Add multiple observations per subject and per condition, and (b) ensure that all subjects and conditions have the same number of observations.\n',
UserWarning,
stacklevel=2)
continue
return only_single
class ANOVA1rm(_Design):
def __init__(self, A, SUBJ):
self.X = None #design matrix
self.S = Factor(SUBJ) #subjects
self.A = Factor(A) #factor levels
self.J = self.A.J #number of observations
self.contrasts = None #contrast matrix
self.term_labels = ['Intercept', 'A', 'S', 'SA']
self.f_terms = [('A','SA')]
self._check_balanced()
self._assemble()
def _assemble(self):
### assemble design matrix columns:
XCONST = self._get_column_const()
XA = self.A.get_design_main()
XS = self.S.get_design_main()
XSA = self.A.get_design_interaction(self.S)
### specify builder and add design matrix columns:
builder = DesignBuilder(labels=self.term_labels)
builder.add_main_columns('Intercept', XCONST)
builder.add_main_columns('A', XA)
builder.add_main_columns('S', XS)
builder.add_main_columns('SA', XSA)
### assemble design matrix and contrasts:
self.X = builder.get_design_matrix()
self.contrasts = builder.get_contrasts()
def _check_balanced(self):
if not (self.A.balanced and self.S.balanced):
raise( ValueError('Design must be balanced.') )
if not self.S.check_balanced(self.A):
raise( ValueError('Design must be balanced.') )
def check_for_single_responses(self):
A,S = self.A.A, self.S.A
only_single = False
for a in self.A.u:
s = S[(A==a)]
if np.unique(s).size == s.size:
only_single = True
warnings.warn('\nWARNING: Only one observation per subject found. Residuals and inference will be approximate. To avoid approximate residuals: (a) Add multiple observations per subject and per condition, and (b) ensure that all subjects and conditions have the same number of observations.\n', UserWarning, stacklevel=2)
continue
return only_single
class ANOVA3rm(ANOVA3):
'''A, B and C are all RM factors.'''
def __init__(self, A, B, C, SUBJ):
self.X = None
self.A = Factor(A)
self.B = Factor(B)
self.C = Factor(C)
self.S = Factor(SUBJ)
self.J = self.A.J
self.contrasts = None
self.term_labels = [
'Intercept', 'A', 'B', 'C', 'S', 'AB', 'AC', 'BC', 'SA', 'SB',
'SC', 'SAB', 'SAC', 'SBC', 'ABC', 'SABC'
]
self.f_terms = [('A', 'SA'), ('B', 'SB'), ('C', 'SC'), ('AB', 'SAB'),
('AC', 'SAC'), ('BC', 'SBC'), ('ABC', 'SABC')]
self._check_balanced()
self._assemble()
def _assemble(self):
### assemble design matrix columns:
XCONST = self._get_column_const()
XA = self.A.get_design_main()
XB = self.B.get_design_main()
XC = self.C.get_design_main()
XS = self.S.get_design_main()
XAB = self.A.get_design_interaction(self.B)
XAC = self.A.get_design_interaction(self.C)
XBC = self.B.get_design_interaction(self.C)
XSA = self.S.get_design_interaction(self.A)
XSB = self.S.get_design_interaction(self.B)
XSC = self.S.get_design_interaction(self.C)
XSAB = self.S.get_design_interaction_3way(self.A, self.B)
XSAC = self.S.get_design_interaction_3way(self.A, self.C)
XSBC = self.S.get_design_interaction_3way(self.B, self.C)
XABC = self.A.get_design_interaction_3way(self.B, self.C)
XSABC = self.S.get_design_interaction_4way(self.A, self.B, self.C)
### specify builder and add design matrix columns:
builder = DesignBuilder(labels=self.term_labels)
builder.add_main_columns('Intercept', XCONST)
builder.add_main_columns('A', XA)
builder.add_main_columns('B', XB)
builder.add_main_columns('C', XC)
builder.add_main_columns('S', XS)
builder.add_main_columns('AB', XAB)
builder.add_main_columns('AC', XAC)
builder.add_main_columns('BC', XBC)
builder.add_main_columns('SA', XSA)
builder.add_main_columns('SB', XSB)
builder.add_main_columns('SC', XSC)
builder.add_main_columns('SAB', XSAB)
builder.add_main_columns('SAC', XSAC)
builder.add_main_columns('SBC', XSBC)
builder.add_main_columns('ABC', XABC)
builder.add_main_columns('SABC', XSABC)
### assemble design matrix and contrasts:
self.X = builder.get_design_matrix()
self.contrasts = builder.get_contrasts()
def _swapAB(self):
if self._swap:
A, B = self.B, self.A
self.A = A
self.B = B
def _check_balanced(self):
if not (self.A.balanced and self.B.balanced and self.C.balanced
and self.S.balanced):
raise (ValueError('Design must be balanced.'))
if not self.A.check_balanced(self.B):
raise (ValueError('Design must be balanced.'))
if not self.A.check_balanced(self.C):
raise (ValueError('Design must be balanced.'))
if not self.B.check_balanced(self.C):
raise (ValueError('Design must be balanced.'))
if not self.S.check_balanced_rm(self.C):
raise (ValueError('Design must be balanced.'))
def check_for_single_responses(self):
A, B, C, S = self.A.A, self.B.A, self.C.A, self.S.A
only_single = False
for a in self.A.u:
for b in self.B.u:
for c in self.C.u:
s = S[(A == a) & (B == b) & (C == c)]
if np.unique(s).size == s.size:
only_single = True
warnings.warn(
'\nWARNING: Only one observation per subject found. Residuals and inference will be approximate. To avoid approximate residuals: (a) Add multiple observations per subject and per condition, and (b) ensure that all subjects and conditions have the same number of observations.\n',
UserWarning,
stacklevel=2)
continue
return only_single
class ANOVA3rm(ANOVA3):
'''A, B and C are all RM factors.'''
def __init__(self, A, B, C, SUBJ):
self.X = None
self.A = Factor(A)
self.B = Factor(B)
self.C = Factor(C)
self.S = Factor(SUBJ)
self.J = self.A.J
self.contrasts = None
self.term_labels = ['Intercept', 'A','B','C','S', 'AB','AC','BC', 'SA','SB','SC', 'SAB','SAC','SBC', 'ABC', 'SABC']
self.f_terms = [('A','SA'), ('B','SB'), ('C','SC'), ('AB','SAB'), ('AC','SAC'), ('BC','SBC'), ('ABC','SABC')]
self._check_balanced()
self._assemble()
def _assemble(self):
### assemble design matrix columns:
XCONST = self._get_column_const()
XA = self.A.get_design_main()
XB = self.B.get_design_main()
XC = self.C.get_design_main()
XS = self.S.get_design_main()
XAB = self.A.get_design_interaction(self.B)
XAC = self.A.get_design_interaction(self.C)
XBC = self.B.get_design_interaction(self.C)
XSA = self.S.get_design_interaction(self.A)
XSB = self.S.get_design_interaction(self.B)
XSC = self.S.get_design_interaction(self.C)
XSAB = self.S.get_design_interaction_3way(self.A, self.B)
XSAC = self.S.get_design_interaction_3way(self.A, self.C)
XSBC = self.S.get_design_interaction_3way(self.B, self.C)
XABC = self.A.get_design_interaction_3way(self.B, self.C)
XSABC = self.S.get_design_interaction_4way(self.A, self.B, self.C)
### specify builder and add design matrix columns:
builder = DesignBuilder(labels=self.term_labels)
builder.add_main_columns('Intercept', XCONST)
builder.add_main_columns('A', XA)
builder.add_main_columns('B', XB)
builder.add_main_columns('C', XC)
builder.add_main_columns('S', XS)
builder.add_main_columns('AB', XAB)
builder.add_main_columns('AC', XAC)
builder.add_main_columns('BC', XBC)
builder.add_main_columns('SA', XSA)
builder.add_main_columns('SB', XSB)
builder.add_main_columns('SC', XSC)
builder.add_main_columns('SAB', XSAB)
builder.add_main_columns('SAC', XSAC)
builder.add_main_columns('SBC', XSBC)
builder.add_main_columns('ABC', XABC)
builder.add_main_columns('SABC', XSABC)
### assemble design matrix and contrasts:
self.X = builder.get_design_matrix()
self.contrasts = builder.get_contrasts()
def _swapAB(self):
if self._swap:
A,B = self.B, self.A
self.A = A
self.B = B
def _check_balanced(self):
if not (self.A.balanced and self.B.balanced and self.C.balanced and self.S.balanced):
raise( ValueError('Design must be balanced.') )
if not self.A.check_balanced(self.B):
raise( ValueError('Design must be balanced.') )
if not self.A.check_balanced(self.C):
raise( ValueError('Design must be balanced.') )
if not self.B.check_balanced(self.C):
raise( ValueError('Design must be balanced.') )
if not self.S.check_balanced_rm(self.C):
raise( ValueError('Design must be balanced.') )
def check_for_single_responses(self):
A,B,C,S = self.A.A, self.B.A, self.C.A, self.S.A
only_single = False
for a in self.A.u:
for b in self.B.u:
for c in self.C.u:
s = S[(A==a) & (B==b) & (C==c)]
if np.unique(s).size == s.size:
only_single = True
warnings.warn('\nWARNING: Only one observation per subject found. Residuals and inference will be approximate. To avoid approximate residuals: (a) Add multiple observations per subject and per condition, and (b) ensure that all subjects and conditions have the same number of observations.\n', UserWarning, stacklevel=2)
continue
return only_single