The `mipo`

object contains the results of the pooling step.
The function `pool`

generates an object of class `mipo`

.

## Usage

```
mipo(mira.obj, ...)
# S3 method for mipo
summary(
object,
type = c("tests", "all"),
conf.int = FALSE,
conf.level = 0.95,
exponentiate = FALSE,
...
)
# S3 method for mipo
print(x, ...)
# S3 method for mipo.summary
print(x, ...)
process_mipo(z, x, conf.int = FALSE, conf.level = 0.95, exponentiate = FALSE)
```

## Arguments

- mira.obj
An object of class

`mira`

- ...
Arguments passed down

- object
An object of class

`mipo`

- conf.int
Logical indicating whether to include a confidence interval. The default is

`FALSE`

.- conf.level
Confidence level of the interval, used only if

`conf.int = TRUE`

. Number between 0 and 1.- exponentiate
Flag indicating whether to exponentiate the coefficient estimates and confidence intervals (typical for logistic regression).

- x
An object of class

`mipo`

- z
Data frame with a tidied version of a coefficient matrix

## Details

An object class `mipo`

is a `list`

with
elements: `call`

, `m`

, `pooled`

and `glanced`

.

The `pooled`

elements is a data frame with columns:

`estimate` | Pooled complete data estimate |

`ubar` | Within-imputation variance of `estimate` |

`b` | Between-imputation variance of `estimate` |

`t` | Total variance, of `estimate` |

`dfcom` | Degrees of freedom in complete data |

`df` | Degrees of freedom of $t$-statistic |

`riv` | Relative increase in variance |

`lambda` | Proportion attributable to the missingness |

`fmi` | Fraction of missing information |

The names of the terms are stored as `row.names(pooled)`

.

The `glanced`

elements is a `data.frame`

with `m`

rows.
The precise composition depends on the class of the complete-data analysis.
At least field `nobs`

is expected to be present.

The `process_mipo`

is a helper function to process a
tidied mipo object, and is normally not called directly.
It adds a confidence interval, and optionally exponentiates, the result.

## References

van Buuren S and Groothuis-Oudshoorn K (2011). `mice`

:
Multivariate Imputation by Chained Equations in `R`

. *Journal of
Statistical Software*, **45**(3), 1-67.
doi:10.18637/jss.v045.i03