R: Multiple imputation pooling: univariate version

pool.scalar {mice} | R Documentation

## Multiple imputation pooling: univariate version

### Description

Pools univariate estimates of m repeated complete data analysis

### Usage

    pool.scalar(Q, U, n = Inf, k = 1)

### Arguments

`Q` | A vector of univariate estimates of `m` repeated complete data analyses.
`U` | A vector containing the corresponding `m` variances of the univariate estimates.
`n` | A number providing the sample size. If nothing is specified, an infinite sample `n = Inf` is assumed.
`k` | A number indicating the number of parameters to be estimated. By default, `k = 1` is assumed.

### Details

The function averages the univariate estimates of the complete data model, computes the total variance over the repeated analyses, and computes the relative increase in variance due to nonresponse and the fraction of missing information.

### Value

Returns a list with components.

`m`:

  Number of imputations.

`qhat`:

  The `m` univariate estimates of repeated complete-data analyses.

`u`:

  The corresponding `m` variances of the univariate estimates.

`qbar`:

  The pooled univariate estimate, formula (3.1.2) Rubin (1987).

`ubar`:

  The mean of the variances (i.e. the pooled within-imputation variance), formula (3.1.3) Rubin (1987).

`b`:

  The between-imputation variance, formula (3.1.4) Rubin (1987).

`t`:

  The total variance of the pooled estimated, formula (3.1.5) Rubin (1987).

`r`:

  The relative increase in variance due to nonresponse, formula (3.1.7) Rubin (1987).

`df`:

  The degrees of freedom for t reference distribution by the method of Barnard-Rubin (1999).

`fmi`:

  The fraction missing information due to nonresponse, formula (3.1.10) Rubin (1987).

### Author(s)

Karin Groothuis-Oudshoorn and Stef van Buuren, 2009

### References

Rubin, D.B. (1987). Multiple Imputation for Nonresponse in Surveys. New York: John Wiley and Sons.

### See Also

`pool`

### Examples

    # example with manual and automatic pooling
    imp <- mice(nhanes, maxit = 2, m = 2, print = FALSE, seed = 18210)
    fit <- with(data = imp, lm(bmi ~ age))

    # manual pooling
    summary(fit$analyses[[1]])
    summary(fit$analyses[[2]])
    pool.scalar(Q = c(-1.5457, -1.428), U = c(0.9723^2, 1.041^2), n = 25, k = 2)

    # automatic pooling using broom
    pool(fit)

[Package mice version 3.13.0 Index]