Purpose in LIfe (MLQ-P) with Imputed Data

Loading the dataset

data.test4 <- read.csv("/Volumes/TOSHIBA EXT/Dropbox/ADULT STUDY/adult_study011615.csv")
# Load the psych package
library(psych)
items <- c("MLQ1" ,"MLQ4", "MLQ5", "MLQ6", "MLQ9")
scaleKey <- c(1, 1, 1,1,-1)
data.test4$meanmlq  <- scoreItems(scaleKey, items=data.test4[,items], delete=FALSE)$score

library(reshape2); library(car); library(Amelia);library(mitools);library(nlme)

## 
## Attaching package: 'car'
## 
## The following object is masked from 'package:psych':
## 
##     logit
## 
## Loading required package: Rcpp
## ## 
## ## Amelia II: Multiple Imputation
## ## (Version 1.7.3, built: 2014-11-14)
## ## Copyright (C) 2005-2015 James Honaker, Gary King and Matthew Blackwell
## ## Refer to http://gking.harvard.edu/amelia/ for more information
## ##

data <- data.test4[,c("ID", "GROUP", "wave", "meanmlq")]
data <- dcast(data, ID + GROUP ~ wave, mean, value.var = "meanmlq")
data[,3:5] <- apply(data[,3:5],2,function(x) recode(x, "NaN = NA") )

Create new data set with ID Group baseline meanmlq and wave so that we have Baseline, time 1 and 2 to compare to

data2 <- as.data.frame(mapply(c,data[,1:4], data[,c(1:3,5)]))
data2$wave <- rep(1:2, each=89)
names(data2) <- c("ID", "GROUP", "BASELINE", "meanmlq", "WAVE")

Imputing missing data

MI <- amelia(data2, 5, idvars = c("GROUP","ID","WAVE"))

## -- Imputation 1 --
## 
##   1  2  3  4  5  6
## 
## -- Imputation 2 --
## 
##   1  2  3  4  5  6  7  8
## 
## -- Imputation 3 --
## 
##   1  2  3  4  5  6  7  8
## 
## -- Imputation 4 --
## 
##   1  2  3  4  5  6  7  8
## 
## -- Imputation 5 --
## 
##   1  2  3  4  5  6  7  8  9 10 11

Creating new dataset with missing data imputed

data(MI$imputations)

## Warning in data(MI$imputations): data set 'MI$imputations' not found

allimplogreg<-lapply(MI$imputations,function(X) {lme(meanmlq ~ GROUP * WAVE + BASELINE, random = ~1 | ID, data = X, method = "ML", na.action = "na.omit")})

betas<-MIextract(allimplogreg, fun=fixef)
vars<-MIextract(allimplogreg, fun=vcov)
summary(MIcombine(betas,vars))

## Multiple imputation results:
##       MIcombine.default(betas, vars)
##                results         se      (lower    upper) missInfo
## (Intercept)  1.8771564 0.37988529  1.11657007 2.6377428     29 %
## GROUP        0.7041079 0.31416749  0.08636766 1.3218482     11 %
## WAVE         0.1484360 0.16712889 -0.18350042 0.4803724     23 %
## BASELINE     0.6440744 0.06499475  0.50667535 0.7814734     54 %
## GROUP:WAVE  -0.2819679 0.21319009 -0.70810618 0.1441703     28 %

library("Zelig")

## Loading required package: boot
## 
## Attaching package: 'boot'
## 
## The following object is masked from 'package:car':
## 
##     logit
## 
## The following object is masked from 'package:psych':
## 
##     logit
## 
## Loading required package: MASS
## Loading required package: sandwich
## ZELIG (Versions 4.2-1, built: 2013-09-12)
## 
## +----------------------------------------------------------------+
## |  Please refer to http://gking.harvard.edu/zelig for full       |
## |  documentation or help.zelig() for help with commands and      |
## |  models support by Zelig.                                      |
## |                                                                |
## |  Zelig project citations:                                      |
## |    Kosuke Imai, Gary King, and Olivia Lau.  (2009).            |
## |    ``Zelig: Everyone's Statistical Software,''                 |
## |    http://gking.harvard.edu/zelig                              |
## |   and                                                          |
## |    Kosuke Imai, Gary King, and Olivia Lau. (2008).             |
## |    ``Toward A Common Framework for Statistical Analysis        |
## |    and Development,'' Journal of Computational and             |
## |    Graphical Statistics, Vol. 17, No. 4 (December)             |
## |    pp. 892-913.                                                |
## |                                                                |
## |   To cite individual Zelig models, please use the citation     |
## |   format printed with each model run and in the documentation. |
## +----------------------------------------------------------------+
## 
## 
## 
## Attaching package: 'Zelig'
## 
## The following objects are masked from 'package:psych':
## 
##     alpha, describe, sim
## 
## The following object is masked from 'package:utils':
## 
##     cite

zelig.fit <- zelig(meanmlq ~ GROUP * WAVE + BASELINE, random = ~1 | ID, data = MI$imputations,  model = "ls", cite = FALSE)
summary(zelig.fit)

## 
##   Model: ls
##   Number of multiply imputed data sets: 5 
## 
## Combined results:
## 
## Call:
## lm(formula = formula, weights = weights, model = F, data = data)
## 
## Coefficients:
##                  Value Std. Error     t-stat      p-value
## (Intercept)  1.8715064 0.38707477  4.8349997 9.219012e-06
## GROUP        0.7043391 0.34112902  2.0647292 3.944287e-02
## WAVE         0.1484324 0.18299384  0.8111335 4.187475e-01
## BASELINE     0.6452639 0.06257446 10.3119372 3.725151e-08
## GROUP:WAVE  -0.2819854 0.23232299 -1.2137644 2.280970e-01
## 
## For combined results from datasets i to j, use summary(x, subset = i:j).
## For separate results, use print(summary(x), subset = i:j).