mice.impute.norm.nob

norm.nob

Incomplete data vector of length <code>n</code>

Vector of missing data pattern (<code>FALSE</code>=missing,
<code>TRUE</code>=observed)

Matrix (<code>n</code> x <code>p</code>) of complete covariates.


Imputes univariate missing data using linear regression analysis (non
Bayesian version)


datagen

Multiple imputation using Fully Conditional Specification (FCS)
implemented by the MICE algorithm. Each variable has its own imputation
model. Built-in imputation models are provided for continuous data
(predictive mean matching, normal), binary data (logistic regression),
unordered categorical data (polytomous logistic regression) and ordered
categorical data (proportional odds). MICE can also impute continuous
two-level data (normal model, pan, second-level variables). Passive
imputation can be used to maintain consistency between variables. Various
diagnostic plots are available to inspect the quality of the imputations.

Stef van Buuren

mice

Multivariate Imputation by Chained Equations

mice.impute.norm.nob function

The function does not incorporate the variability of the
regression weights, so it is not 'proper' in the sense of Rubin. For small
samples, variability of the imputed data is therefore underestimated.

mice.impute.norm.nob: Imputation by linear regression (non Bayesian)

Description

Usage

Arguments

Value

Warning

Details

References

See Also