Which is the best package for multiple imputation in R?

Which is the best package for multiple imputation in R?

The mice package allows us to create a number of imputed datasets that replace missing values with plausible values and conduct our analysis on these separate, complete datasets in order to obtain one regression coefficient. There are two approaches to multiple imputation, implemented by different packages in R:

How to impute data with mice for multiple imputation?

The little-MCAR-Test will only test all data and discard MCAR. Also, it has assumptions like normality, so if your data doesn’t meet them, the test might tell you it’s not mcar even if it is. The “MissMech” package in R has tests to show if assumptions are met.

How does the MICE package deal with missing data?

The mice package implements a method to deal with missing data. The package creates multiple imputations (replacement values) for multivariate missing data. The method is based on Fully Conditional Specification, where each incomplete variable is imputed by a separate model.

Where are multiply imputation datasets stored in R?

These complete datasets are stored in an object class called mids, short for multiply imputed dataset. These datasets are copies of the original dataframe except that missing values are now replaced with values generated by mice.

When to use multiple imputation in linear regression?

We will be performing multiple imputation to account appropriately for missingness in the predictors with missing values. The mice package provides several approaches we can use for imputation in building models of all kinds.

How to calculate the results of multiple imputation?

Results in each imputed dataset are shown in the rows starting with number 1 to 3. In the last row which is indicated as “Pooled”, the summary estimates of the mean differences, standard errors, p-values and 95% Confidence Interval are presented. For these calculations Rubin´s Rules are used.

Why does multiple imputation fail in high multicollinearity?

High multicollinearity: Multiple imputation doesn’t like variables that are highly correlated with each other. In most cases, the mice algorithm will leave these variables out of the imputation process. However, in some cases, multiple imputation might fail to start from the beginning.

How to impute a missing value in mice?

Therefore, we decide to impute the missing values. As the first step, the mice command creates several complete datasets (in the figure above, n=3). It considers each missing value to follow a specific distribution, and draws from this distribution a plausible value to replace the missing value.

How does multivariate normal distribution multiple imputation work?

Joint Multivariate Normal Distribution Multiple Imputation: The main assumption in this technique is that the observed data follows a multivariate normal distribution. Therefore, the algorithm that R packages use to impute the missing values draws values from this assumed distribution. Amelia and norm packages use this technique.