def make_hypotheses_matrices(model_results, test_formula):
"""
"""
from patsy.constraint import linear_constraint
exog_names = model_results.model.exog_names
LC = linear_constraint(test_formula, exog_names)
return LC
def make_hypotheses_matrices(model_results, test_formula):
"""
"""
from patsy.constraint import linear_constraint
exog_names = model_results.model.exog_names
LC = linear_constraint(test_formula, exog_names)
return LC
def linear_constraint(self, constraint_likes):
"""Construct a linear constraint in matrix form from a (possibly
symbolic) description.
Possible inputs:
* A dictionary which is taken as a set of equality constraint. Keys
can be either string column names, or integer column indexes.
* A string giving a arithmetic expression referring to the matrix
columns by name.
* A list of such strings which are ANDed together.
* A tuple (A, b) where A and b are array_likes, and the constraint is
Ax = b. If necessary, these will be coerced to the proper
dimensionality by appending dimensions with size 1.
The string-based language has the standard arithmetic operators, / * +
- and parentheses, plus "=" is used for equality and "," is used to
AND together multiple constraint equations within a string. You can
If no = appears in some expression, then that expression is assumed to
be equal to zero. Division is always float-based, even if
``__future__.true_division`` isn't in effect.
Returns a :class:`LinearConstraint` object.
Examples::
di = DesignInfo(["x1", "x2", "x3"])
# Equivalent ways to write x1 == 0:
di.linear_constraint({"x1": 0}) # by name
di.linear_constraint({0: 0}) # by index
di.linear_constraint("x1 = 0") # string based
di.linear_constraint("x1") # can leave out "= 0"
di.linear_constraint("2 * x1 = (x1 + 2 * x1) / 3")
di.linear_constraint(([1, 0, 0], 0)) # constraint matrices
# Equivalent ways to write x1 == 0 and x3 == 10
di.linear_constraint({"x1": 0, "x3": 10})
di.linear_constraint({0: 0, 2: 10})
di.linear_constraint({0: 0, "x3": 10})
di.linear_constraint("x1 = 0, x3 = 10")
di.linear_constraint("x1, x3 = 10")
di.linear_constraint(["x1", "x3 = 0"]) # list of strings
di.linear_constraint("x1 = 0, x3 - 10 = x1")
di.linear_constraint([[1, 0, 0], [0, 0, 1]], [0, 10])
# You can also chain together equalities, just like Python:
di.linear_constraint("x1 = x2 = 3")
"""
return linear_constraint(constraint_likes, self.column_names)
def linear_constraint(self, constraint_likes):
"""Construct a linear constraint in matrix form from a (possibly
symbolic) description.
Possible inputs:
* A dictionary which is taken as a set of equality constraint. Keys
can be either string column names, or integer column indexes.
* A string giving a arithmetic expression referring to the matrix
columns by name.
* A list of such strings which are ANDed together.
* A tuple (A, b) where A and b are array_likes, and the constraint is
Ax = b. If necessary, these will be coerced to the proper
dimensionality by appending dimensions with size 1.
The string-based language has the standard arithmetic operators, / * +
- and parentheses, plus "=" is used for equality and "," is used to
AND together multiple constraint equations within a string. You can
If no = appears in some expression, then that expression is assumed to
be equal to zero. Division is always float-based, even if
``__future__.true_division`` isn't in effect.
Returns a :class:`LinearConstraint` object.
Examples::
di = DesignInfo(["x1", "x2", "x3"])
# Equivalent ways to write x1 == 0:
di.linear_constraint({"x1": 0}) # by name
di.linear_constraint({0: 0}) # by index
di.linear_constraint("x1 = 0") # string based
di.linear_constraint("x1") # can leave out "= 0"
di.linear_constraint("2 * x1 = (x1 + 2 * x1) / 3")
di.linear_constraint(([1, 0, 0], 0)) # constraint matrices
# Equivalent ways to write x1 == 0 and x3 == 10
di.linear_constraint({"x1": 0, "x3": 10})
di.linear_constraint({0: 0, 2: 10})
di.linear_constraint({0: 0, "x3": 10})
di.linear_constraint("x1 = 0, x3 = 10")
di.linear_constraint("x1, x3 = 10")
di.linear_constraint(["x1", "x3 = 0"]) # list of strings
di.linear_constraint("x1 = 0, x3 - 10 = x1")
di.linear_constraint([[1, 0, 0], [0, 0, 1]], [0, 10])
# You can also chain together equalities, just like Python:
di.linear_constraint("x1 = x2 = 3")
"""
return linear_constraint(constraint_likes, self.column_names)
