# datalist2mids: Converting a List of Multiply Imputed Data Sets into a 'mids'... In miceadds: Some Additional Multiple Imputation Functions, Especially for 'mice'

 datlist2mids R Documentation

## Converting a List of Multiply Imputed Data Sets into a `mids` Object

### Description

This function converts a list of multiply imputed data sets to a `mice::mids` object.

### Usage

``````datlist2mids(dat.list, progress=FALSE)
datalist2mids(dat.list, progress=FALSE)
``````

### Arguments

 `dat.list` List of multiply imputed data sets or an object of class `imputationList` (see `mitools::imputationList` ) `progress` An optional logical indicating whether conversion process be displayed

### Value

An object of class `mids`

See `mice::as.mids` for converting a multiply imputed dataset in long format into a `mids` object.

### Examples

``````## Not run:
#############################################################################
# EXAMPLE 1: Imputation of NHANES data using Amelia package
#############################################################################

library(mice)
library(Amelia)

data(nhanes,package="mice")
set.seed(566)  # fix random seed

# impute 10 datasets using Amelia
a.out <- Amelia::amelia(x=nhanes, m=10)
# plot of observed and imputed data
plot(a.out)

# convert list of multiply imputed datasets into a mids object

# linear regression: apply mice functionality lm.mids
mod <- with( a.mids, stats::lm( bmi ~ age ) )
summary( mice::pool( mod ) )
##                    est       se         t       df     Pr(>|t|)     lo 95
##  (Intercept) 30.624652 2.626886 11.658158 8.406608 1.767631e-06 24.617664
##  age         -2.280607 1.323355 -1.723352 8.917910 1.192288e-01 -5.278451
##                   hi 95 nmis       fmi    lambda
##  (Intercept) 36.6316392   NA 0.5791956 0.4897257
##  age          0.7172368    0 0.5549945 0.4652567

# fit linear regression model in Zelig
library(Zelig)
mod2 <- Zelig::zelig( bmi ~ age, model="ls", data=a.out, cite=FALSE)
summary(mod2)
##  Model: Combined Imputations
##              Estimate Std.Error z value Pr(>|z|)
##  (Intercept)   30.625     2.627  11.658  0.00000 ***
##  age           -2.281     1.323  -1.723  0.08482
##  ---
##  Signif. codes:  '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# fit linear regression using mitools package
library(mitools)
datimp <- mitools::imputationList(a.out\$imputations)
mod3 <- with( datimp, stats::lm( bmi ~ age ) )
summary( mitools::MIcombine( mod3 ) )
##  Multiple imputation results:
##        with(datimp, stats::lm(bmi ~ age))
##        MIcombine.default(mod3)
##                results       se    (lower     upper) missInfo
##  (Intercept) 30.624652 2.626886 25.304594 35.9447092     51
##  age         -2.280607 1.323355 -4.952051  0.3908368     49

## End(Not run)
``````

miceadds documentation built on May 29, 2024, 11:05 a.m.