Description Usage Arguments Details Value Author(s) References Examples
Generates Multivariate Imputations using sequential regression with L2 penalization.
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x |
A data frame or a matrix containing the incomplete data. Missing
values are coded as |
x.select |
A Boolean flag. If |
pen |
A Boolean flag. If |
maxit |
A scalar giving the number of iterations. The default is 5. |
m |
Number of multiple imputations. The default is |
track |
A Boolean flag. If |
init.method |
Method for initialization of missing values.
|
L2.fix |
Fixed value of ridge penalty (optional) to use for each
imputation model. For default i.e., |
cv |
A Boolean flag. If |
maxL2 |
The maximum value of the tuning parameter for L2 penalization to be used for optimizing the cross-validated likelihood. Default value is $2^10$. |
Generates multiple imputations for incomplete multivariate data by fitting a sequence of regression models using L2 penalty iteratively. Missing data can occur in one or more variables of the data. In each step of the iteration, ridge regression is fitted according to the distributional form of the missing variable taken as a response. All other variables are taken as predictors. If some predictors are incomplete, the most #'recently generated imputations are used to complete the predictors before using them as a predictor.
a list containing the number of imputed datasets, number of iterations used to obtain imputed data, list of multiply imputed datasets, and summary of missing values.
Faisal Maqbool Zahid faisalmz99@yahoo.com.
Zahid, F. M., and Heumann, C. (2018). Multiple imputation with sequential penalized regression. Statistical Methods in Medical Research, 0962280218755574.
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