def build_model_pre(formula, metadata, family, custom_code_lengths):
assert type(formula) == Formula
assert type(metadata) == Metadata
assert type(family) == Family
assert set(allfactors(formula)).issubset(set(col.name for col in metadata.columns))
check_family_matches_response(formula, metadata, family)
p = PopulationPre(coef_names(formula.terms, metadata, custom_code_lengths))
gs = []
for group in formula.groups:
assert all(type(metadata.column(col)) == Categorical for col in group.columns), 'grouping columns must be a factor'
coefs = coef_names(group.terms, metadata, custom_code_lengths)
g = GroupPre(group.columns, metadata.levels(group.columns), coefs, group.corr and len(coefs) > 1)
gs.append(g)
response = ResponsePre(family, nonlocparams(family))
return ModelDescPre(p, gs, response)
def expand_columns(formula, cols):
lookup = make_column_lookup(cols)
return [
lookup.get(factor, RealValued(factor))
for factor in allfactors(formula)
]
def dummy_design(formula, metadata, N):
assert type(formula) == Formula
assert type(metadata) == dict
assert set(allfactors(formula)).issubset(metadata.keys())
return makedata(formula, dummy_df(metadata, N))