moPrep {Amelia} R Documentation

## Prepare Multiple Overimputation Settings

### Description

A function to generate priors for multiple overimputation of a variable measured with error.

### Usage

```moPrep(x, formula, subset, error.proportion,
gold.standard = !missing(subset), error.sd)

## S3 method for class 'molist'
moPrep(x, formula, subset, error.proportion,
gold.standard = FALSE, error.sd)

## Default S3 method:
moPrep(x, formula, subset, error.proportion,
gold.standard = !missing(subset), error.sd)
```

### Arguments

 `x` either a matrix, data.frame, or a object of class "molist" from a previous `moPrep` call. The first two derive the priors from the data given, and the third will derive the priors from the first `moPrep` call and add them to the already defined priors. `formula` a formula describing the nature of the measurement error for the variable. See "Details." `subset` an optional vector specifying a subset of observations which possess measurement error. `error.proportion` an optional vector specifying the fraction of the observed variance that is due to measurement error. `gold.standard` a logical value indicating if values with no measurement error should be used to estimate the measurement error variance. `error.sd` an optional vector specifying the standard error of the measurement error.

### Details

This function generates priors for multiple overimputation of data measured with error. With the `formula` arugment, you can specify which variable has the error, what the mean of the latent data is, and if there are any other proxy measures of the mismeasured variable. The general syntax for the formula is: `errvar ~ mean | proxy`, where `errvar` is the mismeasured variable, `mean` is a formula for the mean of the latent variable (usually just `errvar` itself), and `proxy` is a another mismeasurement of the same latent variable. The proxies are used to estimate the variance of the measurement error.

`subset` and `gold.standard` refer to the the rows of the data which are and are not measured with error. Gold-standard rows are used to estimate the variance of the measurement. error. `error.proportion` is used to estimate the variance of the measurement error by estimating the variance of the mismeasurement and taking the proportion assumed to be due to error. `error.sd` sets the standard error of the measurement error directly.

### Value

An instance of the S3 class "molist" with the following objects:

• priors a four-column matrix of the multiple overimputation priors associated with the data. Each row of the matrix is `c(row,column, prior.mean, prior.sd)`

• overimp a two-column matrix of cells to be overimputed. Each row of the matrix is of the form `c(row, column)`, which indicate the row and column of the cell to be overimputed.

• data the object name of the matrix or data.frame to which priors refer.

Note that `priors` and `overimp` might contain results from multiple calls to `moPrep`, not just the most recent.

### Methods (by class)

• `molist`: Alter existing moPrep output

• `default`: Default call to moPrep

`amelia`

### Examples

``` data(africa)