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

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)  1.71885825 0.35606271  1.0081715 2.4295450     27 %
## GROUP        0.49714575 0.37394464 -0.2827522 1.2770437     49 %
## WAVE         0.09810162 0.15380941 -0.2067397 0.4029429     21 %
## BASELINE     0.67618232 0.04869578  0.5800515 0.7723131     16 %
## GROUP:WAVE  -0.07442722 0.24409225 -0.5931283 0.4442739     56 %