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

## Value

The summary method returns a data frame with summary statistics of the pooled analysis.

## 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

pool, mids, mira