Description Usage Arguments Details Value References See Also Examples

`pool()`

pools estimates from the ana;yses done withi neach imputed dataset. The typical sequence of steps to do a matching procedure on the imputed datasets are:

Impute the missing values using the

`mice()`

function (from the mice package) or the`amelia()`

function (from the Amelia package), resulting in a multiple imputed dataset (an object of the`mids`

or`amelia`

class);Match or weight each imputed dataset using

`matchthem()`

or`weightthem()`

, resulting in an object of the`mimids`

or`wimids`

class;Check the extent of balance of covariates across the matched datasets (using functions in cobalt);

Fit the statistical model of interest on each matched dataset by the

`with()`

function, resulting in an object of the`mimira`

class; andPool the estimates from each model into a single set of estimates and standard errors, resulting in an object of the

`mipo`

class.

1 |

`object` |
An object of the |

`dfcom` |
A positive number representing the degrees of freedom in the data analysis. The default is |

`pool()`

function averages the estimates of the model and computes the total variance over the repeated analyses by Rubin’s rules. It calls `mice::pool()`

after computing the model degrees of freedom.

This function returns an object of the `mipo`

class. Methods for `mipo`

objects (e.g., `print()`

, `summary`

, etc.) are available in mice, which does not need to be attached to use them.

Stef van Buuren and Karin Groothuis-Oudshoorn (2011). `mice`

: Multivariate Imputation by Chained Equations in `R`

. *Journal of Statistical Software*, 45(3): 1-67. https://www.jstatsoft.org/v45/i03/

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | ```
#Loading libraries
library(MatchThem)
library(survey)
#Loading the dataset
data(osteoarthritis)
#Multiply imputing the missing values
imputed.datasets <- mice::mice(osteoarthritis, m = 5)
#Weighting the multiply imputed datasets
weighted.datasets <- weightthem(OSP ~ AGE + SEX + BMI + RAC + SMK,
imputed.datasets,
approach = 'within',
method = 'ps')
#Analyzing the weighted datasets
models <- with(weighted.datasets,
svyglm(KOA ~ OSP, family = quasibinomial))
#Pooling results obtained from analyzing the datasets
results <- pool(models)
summary(results)
``` |

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