mitml.listlong2mitml.list.RdThese functions convert data sets containing multiple imputations in long format to objects of class mitml.list. The resulting object can be used in further analyses.
long2mitml.list(x, split, exclude = NULL)
jomo2mitml.list(x)The function long2mitml.list converts data frames from the long format to mitml.list (i.e., a list of imputed data sets).
In long format, all imputations are contained in a single data frame, where different imputations are denoted by split.
This function splits the data frame into a list of imputed data sets according to split, excluding the values specified by exclude (see the 'Examples').
The jomo2mitml.list function is a special case of long2mitml.list which converts imputations that have been generated with jomo (see the jomo package)).
A list of imputed data sets with class mitml.list.
data(studentratings)
require(jomo)
#> Loading required package: jomo
# impute data using jomo (native functions)
clus <- studentratings[, "ID"]
Y <- studentratings[, c("ReadAchiev", "ReadDis")]
imp <- jomo(Y = Y, clus = clus, nburn = 1000, nbetween = 100, nimp = 5)
#> Clustered data, using functions for two-level imputation.
#> Found 2 continuous outcomes and no categorical. Using function jomo1rancon.
#> ..................................................
#> ..................................................
#> First imputation registered.
#> ..........Imputation number 2 registered
#> ..........Imputation number 3 registered
#> ..........Imputation number 4 registered
#> ..........Imputation number 5 registered
#> The posterior mean of the fixed effects estimates is:
#> X1
#> ReadAchiev 490.241759
#> ReadDis 2.576419
#>
#> The posterior mean of the random effects estimates is:
#> ReadAchiev.Z1 ReadDis.Z1
#> 1001 19.556542308 -0.392606619
#> 1002 5.702009017 0.287692994
#> 1003 7.182495929 -0.032025764
#> 1004 -56.343936662 0.033621798
#> 1005 -31.663276331 0.271950656
#> 1006 -11.040296513 0.073537083
#> 1007 -7.234035434 -0.041832996
#> 1008 3.802871267 -0.066368822
#> 1009 5.983207413 0.097825324
#> 1010 24.943139431 -0.120733860
#> 1011 -22.710482820 0.182884275
#> 1012 -0.733253362 0.094288669
#> 1013 -17.263719141 0.205085931
#> 1014 -2.507948786 -0.021190559
#> 1015 -0.824495435 -0.305414021
#> 1016 23.931497531 0.312544516
#> 1017 0.006719634 0.001494993
#> 1018 20.682478632 -0.068219285
#> 1019 -24.176648861 0.032666285
#> 1020 -55.727074804 0.183073275
#> 1021 32.074970575 0.388137046
#> 1022 34.725924293 -0.180047015
#> 1023 -34.902463834 0.505623137
#> 1024 10.426060024 0.057251335
#> 1025 -5.931612523 -0.285209913
#> 2001 -10.866521388 0.002070899
#> 2002 -38.313099509 0.814281826
#> 2003 -38.448056105 0.125054705
#> 2004 3.045586582 0.069889294
#> 2005 50.906950269 -0.241348361
#> 2006 33.264103528 -0.625301283
#> 2007 28.721918655 -0.184719825
#> 2008 24.963342566 -0.387038543
#> 2009 15.656334264 0.014498920
#> 2010 39.469537976 -0.151313399
#> 2011 7.153069015 0.116840928
#> 2012 36.164349282 -0.487219522
#> 2013 -34.949068606 0.388214141
#> 2014 -12.070350442 -0.200161933
#> 2015 -5.549843355 0.172880502
#> 2016 -1.466150315 -0.076387573
#> 2017 -68.672332623 0.237383886
#> 2018 7.385790091 -0.226429268
#> 2019 32.221805718 -0.008320462
#> 2020 -13.105245503 0.127943303
#> 2021 20.918747873 -0.205702980
#> 2022 13.476550879 0.022211740
#> 2023 -0.091115346 0.240988191
#> 2024 -51.489995131 0.141899480
#> 2025 17.641111650 -0.290146790
#>
#> The posterior mean of the level 1 covariance matrices is:
#> ReadAchiev ReadDis
#> ReadAchiev 7510.69425 -13.9469465
#> ReadDis -13.94695 0.4082988
#>
#> The posterior mean of the level 2 covariance matrix is:
#> ReadAchiev.Z1 ReadDis.Z1
#> ReadAchiev*Z1 1116.26521 -5.1189105
#> ReadDis*Z1 -5.11891 0.1168438
# split imputations
impList <- long2mitml.list(imp, split = "Imputation", exclude = 0)
impList <- jomo2mitml.list(imp)