mice.impute.lasso.logreg {mice} | R Documentation |
Imputation by direct use of lasso logistic regression
Description
Imputes univariate missing binary data using lasso logistic regression with bootstrap.
Usage
mice.impute.lasso.logreg(y, ry, x, wy = NULL, nfolds = 10, ...)
Arguments
y |
Vector to be imputed |
ry |
Logical vector of length |
x |
Numeric design matrix with |
wy |
Logical vector of length |
nfolds |
The number of folds for the cross-validation of the lasso penalty. The default is 10. |
... |
Other named arguments. |
Details
The method consists of the following steps:
For a given y variable under imputation, draw a bootstrap version y* with replacement from the observed cases
y[ry]
, and stores in x* the corresponding values fromx[ry, ]
.Fit a regularised (lasso) logistic regression with y* as the outcome, and x* as predictors. A vector of regression coefficients bhat is obtained. All of these coefficients are considered random draws from the imputation model parameters posterior distribution. Same of these coefficients will be shrunken to 0.
Compute predicted scores for m.d., i.e. logit-1(X bhat)
Compare the score to a random (0,1) deviate, and impute.
The method is based on the Direct Use of Regularized Regression (DURR) proposed by Zhao & Long (2016) and Deng et al (2016).
Value
Vector with imputed data, same type as y
, and of length
sum(wy)
Author(s)
Edoardo Costantini, 2021
References
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.
See Also
Other univariate imputation functions:
mice.impute.cart()
,
mice.impute.lasso.norm()
,
mice.impute.lasso.select.logreg()
,
mice.impute.lasso.select.norm()
,
mice.impute.lda()
,
mice.impute.logreg.boot()
,
mice.impute.logreg()
,
mice.impute.mean()
,
mice.impute.midastouch()
,
mice.impute.mnar.logreg()
,
mice.impute.mpmm()
,
mice.impute.norm.boot()
,
mice.impute.norm.nob()
,
mice.impute.norm.predict()
,
mice.impute.norm()
,
mice.impute.pmm()
,
mice.impute.polr()
,
mice.impute.polyreg()
,
mice.impute.quadratic()
,
mice.impute.rf()
,
mice.impute.ri()