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.
- 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