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## WHAT IS THE ICE command in Stata?

The ice command performs multivariate imputation via chained equations (van Buuren, Boshuizen, and Knook 1999). The mim command analyzes multiply imputed data by performing phases 2 and 3. mim also provides some capabilities for manipulating multiply imputed data.

**What is MI set Stata?**

mi set is used to set a regular Stata dataset to be an mi dataset. mi set is also used to modify the attributes of an already set dataset. An mi set dataset has the following attributes: • The data are recorded in a style: wide, mlong, flong, or flongsep; see [MI] Styles.

**What is Mi estimate?**

mi estimate estimates model parameters from multiply imputed data and adjusts coefficients and. standard errors for the variability between imputations. It runs the specified estimation command on. each of the M imputed datasets to obtain the M completed-data estimates of coefficients and their. VCEs.

### What is multiple imputation by chained equations?

Multiple Imputation by Chained Equations is a robust, informative method of dealing with missing data in datasets. The procedure ‘fills in’ (imputes) missing data in a dataset through an iterative series of predictive models. This process is continued until all specified variables have been imputed.

**Why is Stata being so slow?**

Why is Stata running very slowly? Stata is using more memory than is physically available on your computer. A clear indicator is constant, prolonged disk access during the execution of a command. Possible solutions include adding more memory to your computer or moving to a 64-bit machine.

**How many imputations are really needed?**

However, it is important to know how many imputations are necessary before MI and FIML are sufficiently equivalent in ways that are important to prevention scientists. MI theory suggests that small values of m, even on the order of three to five imputations, yield excellent results.

#### What is multiple imputation for missing data?

Multiple imputation is a general approach to the problem of missing data that is available in several commonly used statistical packages. It aims to allow for the uncertainty about the missing data by creating several different plausible imputed data sets and appropriately combining results obtained from each of them.

**How do you conduct multiple imputations?**

It has four steps:

- Create m sets of imputations for the missing values using an imputation process with a random component.
- The result is m full data sets.
- Analyze each completed data set.
- Combine results, calculating the variation in parameter estimates.

**What is missForest?**

missForest is a nonparametric imputation method for basically any kind of data. It can cope with mixed-type of variables, nonlinear relations, complex interactions and high dimensionality (p ≫ n). It only requires the observation (i.e. the rows of the data frame supplied to the function) to be pairwise independent.

## Do files in Stata?

The do-file contains the Stata commands that you wish to execute. Executing a do-file is the same as executing a series of commands interactively, only you have a permanent record of your commands. This allows you to quickly reproduce work you have already done and go from there.

**Can you undo in Stata?**

It’s also very difficult to recover from mistakes—there’s no “undo” command in Stata. A do file contains the same commands you’d type in interactive Stata, but since they’re written in a permanent file they can be debugged or modified and then rerun at will.