statsmodels.imputation.bayes_mi.MI¶
-
class
statsmodels.imputation.bayes_mi.
MI
(imp, model, model_args_fn=None, model_kwds_fn=None, formula=None, fit_args=None, fit_kwds=None, xfunc=None, burn=100, nrep=20, skip=10)[source]¶ MI performs multiple imputation using a provided imputer object.
- Parameters
- imp
object
An imputer class, such as BayesGaussMI.
- model
model
class
Any statsmodels model class.
- model_args_fn
function
A function taking an imputed dataset as input and returning endog, exog. If the model is fit using a formula, returns a DataFrame used to build the model. Optional when a formula is used.
- model_kwds_fn
function
,optional
A function taking an imputed dataset as input and returning a dictionary of model keyword arguments.
- formula
str
,optional
If provided, the model is constructed using the from_formula class method, otherwise the __init__ method is used.
- fit_argslist-like,
optional
List of arguments to be passed to the fit method
- fit_kwdsdict-like,
optional
Keyword arguments to be passed to the fit method
- xfunc
function
mapping
ndarray
to
ndarray
A function that is applied to the complete data matrix prior to fitting the model
- burn
int
Number of burn-in iterations
- nrep
int
Number of imputed data sets to use in the analysis
- skip
int
Number of Gibbs iterations to skip between successive multiple imputation fits.
- imp
Notes
The imputer object must have an ‘update’ method, and a ‘data’ attribute that contains the current imputed dataset.
xfunc can be used to introduce domain constraints, e.g. when imputing binary data the imputed continuous values can be rounded to 0/1.
Methods
fit
([results_cb])Impute datasets, fit models, and pool results.