def test_get_etas(pheno_path, testdata):
model = Model(pheno_path)
etas = _get_etas(model, ['ETA(1)'])
assert len(etas) == 1
etas = _get_etas(model, ['ETA(1)', 'CL'], include_symbols=True)
assert len(etas) == 1
etas = _get_etas(model, ['ETA(1)', 'V'], include_symbols=True)
assert len(etas) == 2
with pytest.raises(KeyError):
_get_etas(model, ['ETA(23)'])
model = Model(testdata / 'nonmem' / 'pheno_block.mod')
rvs = _get_etas(model, None)
assert rvs[0].name == 'ETA(1)'
model.parameters.fix = {'OMEGA(1,1)': True}
rvs = _get_etas(model, None)
assert rvs[0].name == 'ETA(2)'
model = Model(testdata / 'nonmem' / 'pheno_block.mod')
model.random_variables['ETA(1)'].level = 'IOV'
rvs = _get_etas(model, None)
assert rvs[0].name == 'ETA(2)'
def split_rv_block(model, list_of_rvs=None):
"""
Splits a block structure given a list of etas to separate.
Parameters
----------
model : Model
Pharmpy model to create block effect on.
list_of_rvs : str, list
Name/names of etas to split from block structure. If None, all etas that are IIVs and
non-fixed will become single. None is default.
"""
rvs = model.random_variables
list_of_rvs = _get_etas(model, list_of_rvs)
parameters_before = rvs.parameter_names
rvs.unjoin(list_of_rvs)
parameters_after = rvs.parameter_names
removed_parameters = set(parameters_before) - set(parameters_after)
pset = model.parameters
for param in removed_parameters:
del pset[param]
return model
def tdist(model, list_of_etas=None):
"""
Applies a t-distribution transformation to specified etas from a :class:`pharmpy.model`. Initial
estimate for degrees of freedom is 80 with bounds (3, 100).
Parameters
----------
model : Model
Pharmpy model to apply t distribution transformation to.
list_of_etas : str, list
Name/names of etas to transform. If None, all etas will be transformed (default).
"""
list_of_etas = _format_input_list(list_of_etas)
etas = _get_etas(model, list_of_etas)
eta_transformation = EtaTransformation.tdist(len(etas))
_transform_etas(model, eta_transformation, etas)
return model
def boxcox(model, list_of_etas=None):
"""
Applies a boxcox transformation to specified etas from a :class:`pharmpy.model`. Initial
estimate for lambda is 0.1 with bounds (-3, 3).
Parameters
----------
model : Model
Pharmpy model to apply boxcox transformation to.
list_of_etas : str, list
Name/names of etas to transform. If None, all etas will be transformed (default).
"""
list_of_etas = _format_input_list(list_of_etas)
etas = _get_etas(model, list_of_etas)
eta_transformation = EtaTransformation.boxcox(len(etas))
_transform_etas(model, eta_transformation, etas)
return model
def john_draper(model, list_of_etas=None):
"""
Applies a John Draper transformation [1]_ to specified etas from a
:class:`pharmpy.model`. Initial estimate for lambda is 0.1 with bounds (-3, 3).
.. [1] John, J., Draper, N. (1980). An Alternative Family of Transformations.
Journal of the Royal Statistical Society. Series C (Applied Statistics),
29(2), 190-197. doi:10.2307/2986305
Parameters
----------
model : Model
Pharmpy model to apply John Draper transformation to.
list_of_etas : str, list
Name/names of etas to transform. If None, all etas will be transformed (default).
"""
list_of_etas = _format_input_list(list_of_etas)
etas = _get_etas(model, list_of_etas)
eta_transformation = EtaTransformation.john_draper(len(etas))
_transform_etas(model, eta_transformation, etas)
return model
def add_iov(model, occ, list_of_parameters=None, eta_names=None):
"""
Adds IOVs to :class:`pharmpy.model`. Initial estimate of new IOVs are 10% of the IIV eta
it is based on.
Parameters
----------
model : Model
Pharmpy model to add new IOVs to.
occ : str
Name of occasion column.
list_of_parameters : str, list
List of names of parameters and random variables. Accepts random variable names, parameter
names, or a mix of both.
eta_names: str, list
Custom names of new etas. Must be equal to the number of input etas times the number of
categories for occasion.
"""
rvs, pset, sset = model.random_variables, model.parameters, model.statements
list_of_parameters = _format_input_list(list_of_parameters)
etas = _get_etas(model, list_of_parameters, include_symbols=True)
categories = _get_occ_levels(model.dataset, occ)
if eta_names and len(eta_names) != len(etas) * len(categories):
raise ValueError(
f'Number of provided names incorrect, need {len(etas) * len(categories)} names.'
)
elif len(categories) == 1:
raise ValueError(f'Only one value in {occ} column.')
iovs, etais = ModelStatements(), ModelStatements()
for i, eta in enumerate(etas, 1):
omega_name = str(
next(iter(eta.sympy_rv.pspace.distribution.free_symbols)))
omega = S(f'OMEGA_IOV_{i}') # TODO: better name
pset.append(Parameter(str(omega), init=pset[omega_name].init * 0.1))
iov = S(f'IOV_{i}')
values, conditions = [], []
for j, cat in enumerate(categories, 1):
if eta_names:
eta_name = eta_names[j - 1]
else:
eta_name = f'ETA_IOV_{i}{j}'
eta_new = RandomVariable.normal(eta_name, 'iov', 0, omega)
rvs.append(eta_new)
values += [S(eta_new.name)]
conditions += [Eq(cat, S(occ))]
expression = Piecewise(*zip(values, conditions))
iovs.append(Assignment(iov, sympy.sympify(0)))
iovs.append(Assignment(iov, expression))
etais.append(Assignment(S(f'ETAI{i}'), eta.symbol + iov))
sset.subs({eta.name: S(f'ETAI{i}')})
iovs.extend(etais)
iovs.extend(sset)
model.random_variables, model.parameters, model.statements = rvs, pset, iovs
return model