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Imputes univariate missing data using logistic regression following a preprocessing lasso variable selection step.

Usage, ry, x, wy = NULL, nfolds = 10, ...)



Vector to be imputed


Logical vector of length length(y) indicating the the subset y[ry] of elements in y to which the imputation model is fitted. The ry generally distinguishes the observed (TRUE) and missing values (FALSE) in y.


Numeric design matrix with length(y) rows with predictors for y. Matrix x may have no missing values.


Logical vector of length length(y). A TRUE value indicates locations in y for which imputations are created.


The number of folds for the cross-validation of the lasso penalty. The default is 10.


Other named arguments.


Vector with imputed data, same type as y, and of length sum(wy)


The method consists of the following steps:

  1. For a given y variable under imputation, fit a linear regression with lasso penalty using y[ry] as dependent variable and x[ry, ] as predictors. The coefficients that are not shrunk to 0 define the active set of predictors that will be used for imputation.

  2. Fit a logit with the active set of predictors, and find (bhat, V(bhat))

  3. Draw BETA from N(bhat, V(bhat))

  4. Compute predicted scores for m.d., i.e. logit-1(X BETA)

  5. Compare the score to a random (0,1) deviate, and impute.

The user can specify a predictorMatrix in the mice call to define which predictors are provided to this univariate imputation method. The lasso regularization will select, among the variables indicated by the user, the ones that are important for imputation at any given iteration. Therefore, users may force the exclusion of a predictor from a given imputation model by speficing a 0 entry. However, a non-zero entry does not guarantee the variable will be used, as this decision is ultimately made by the lasso variable selection procedure.

The method is based on the Indirect Use of Regularized Regression (IURR) proposed by Zhao & Long (2016) and Deng et al (2016).


Deng, Y., Chang, C., Ido, M. S., & Long, Q. (2016). Multiple imputation for general missing data patterns in the presence of high-dimensional data. Scientific reports, 6(1), 1-10.

Zhao, Y., & Long, Q. (2016). Multiple imputation in the presence of high-dimensional data. Statistical Methods in Medical Research, 25(5), 2021-2035.


Edoardo Costantini, 2021