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

## Description

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

## Usage

 ```1 2``` ```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.
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53``` ```############################################################################# # 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 a.mids <- miceadds::datlist2mids( a.out\$imputations ) # 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 ## Not run: # 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) ```